WO2021135371A1

WO2021135371A1 - Automatic driving method, related device and computer-readable storage medium

Info

Publication number: WO2021135371A1
Application number: PCT/CN2020/114265
Authority: WO
Inventors: 刘继秋; 伍勇; 刘建琴
Original assignee: 华为技术有限公司
Priority date: 2019-12-31
Filing date: 2020-09-09
Publication date: 2021-07-08
Also published as: US20220332348A1; JP2023508114A; EP4071661A1; EP4071661A4; JP7540641B2; CN113128303A

Abstract

An automatic driving method, a related device and a computer-readable storage medium. The method is applied to a cloud server, and the method comprises: receiving vehicle attribute information and travel information of a target vehicle that are reported by the target vehicle (S202), wherein the vehicle attribute information of the target vehicle is used for generating an automatic driving policy; according to the travel information, acquiring, in an automatic driving policy map layer, map layer information of a first road section on which the target vehicle travels (S204); acquiring a first driving policy according to the map layer information of the first road section and the vehicle attribute information of the target vehicle (S206); and sending the first driving policy to the target vehicle (S208). The method can reduce the risks of automatic driving.

Description

Automatic driving method, related equipment and computer readable storage medium

This application requires the priority of a Chinese patent application filed with the China Intellectual Property Office on December 31, 2019, with an application number of 201911425837.X, and an invention title of "An Autonomous Driving Method, Related Equipment, and Computer-readable Storage Medium". The entire content is incorporated into this application by reference.

Technical field

This application relates to the field of artificial intelligence technology, and in particular to an automatic driving method, related equipment, and computer-readable storage media.

Background technique

Artificial Intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results. In other words, artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can react in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machines have the functions of perception, reasoning and decision-making. Research in the field of artificial intelligence includes robotics, natural language processing, computer vision, decision-making and reasoning, human-computer interaction, recommendation and search, and basic AI theories.

Autonomous driving is a mainstream application in the field of artificial intelligence. Autonomous driving technology relies on the collaboration of computer vision, radar, monitoring devices, and global positioning systems to enable motor vehicles to achieve autonomous driving without the need for human active operations. Self-driving vehicles use various computing systems to transport passengers from one location to another. Some autonomous vehicles may require some initial input or continuous input from an operator (such as a driver, a passenger). The self-driving vehicle permits the operator to switch from the manual driving mode to the automatic driving mode or a mode in between. Since autonomous driving technology does not require humans to drive motor vehicles, it can theoretically effectively avoid human drivers’ driving errors, reduce traffic accidents, and improve highway transportation efficiency. Therefore, more and more attention has been paid to autonomous driving technology.

In the existing implementation, by setting sensors on the autonomous vehicle, the sensor can acquire the driving state data of the surrounding vehicles (for example, the speed, acceleration, heading angle, etc.) of the surrounding vehicles, so that the autonomous vehicle can be based on the acquired sensor The data determines the driving strategy that meets the safety requirements. Since data needs to be obtained from sensors, and the data obtained by sensors is often limited, when determining a driving strategy that meets safety requirements based on the obtained data, if the sensor fails or the sensitivity is not high or the accuracy is not high enough, it is easy to produce poor safety This undoubtedly increases the risk of autonomous driving. Therefore, how to improve the accuracy of determining the driving strategy that meets the safety requirements to improve the safety of the safe driving of the vehicle is a technical problem that needs to be solved urgently.

Summary of the invention

The present application provides an automatic driving method, related equipment, and computer-readable storage medium, which can improve the accuracy of determining a driving strategy that meets safety requirements and reduce the risk of automatic driving.

In a first aspect, an automatic driving method is provided, the method is applied to a cloud server, and the method includes: receiving vehicle attribute information reported by a target vehicle and travel information of the target vehicle; wherein the vehicle of the target vehicle The attribute information is used to generate an automatic driving strategy; the layer information of the first road segment on which the target vehicle is traveling is acquired in the automatic driving strategy layer according to the travel information; according to the layer information of the first road segment and the target The vehicle attribute information of the vehicle acquires a first driving strategy; and the first driving strategy is sent to the target vehicle.

To implement the embodiment of this application, the cloud server can obtain the layer information of the first road segment in the automatic driving strategy layer based on the itinerary information reported by the target vehicle, and then, according to the layer information of the first road segment and the attribute information of the vehicle Obtain a driving strategy that meets the safety requirements, so that the target vehicle can drive automatically according to the driving strategy determined by the cloud server. Since the information contained in the autonomous driving strategy layer is richer and can overcome the sensor’s perception defects (for example, the sensor’s perception defects are reflected in the limited data acquired by the sensor, the limited detection range of the sensor, and the detection of the sensor is easily affected by the environment. Etc.), which can improve the accuracy of determining driving strategies that meet safety requirements and reduce the risk of autonomous driving.

In a possible implementation manner, the cloud server stores the corresponding relationship between the driving strategy and the safe passage probability; the first driving is obtained according to the layer information of the first road section and the vehicle attribute information of the target vehicle. The strategy includes: obtaining the first safe passing probability according to the layer information of the first road section and the vehicle attribute information of the target vehicle; obtaining the first safe passing probability according to the corresponding relationship between the driving strategy and the safe passing probability The corresponding first driving strategy. To implement the embodiments of this application, the cloud server can obtain the first safe passage probability according to the layer information of the first road section being driven and the vehicle attribute information of the target vehicle, and then the corresponding relationship between the preset driving strategy and the safe passage probability Acquire the first driving strategy corresponding to the first safe passage probability. Since the layer contains rich information and can overcome the sensor's perception defects, this implementation method can improve the accuracy of determining the driving strategy that meets the safety requirements and reduce the risk of autonomous driving.

In a possible implementation manner, the obtaining the first safe passage probability according to the layer information of the first road section and the vehicle attribute information of the target vehicle includes: calculating the first safe passage through a first model Probability, wherein the first model includes at least one information item and a weight parameter corresponding to the at least one information item, and the at least one information item is a vehicle based on the layer information of the first road section and the target vehicle An information item obtained by extracting attribute information, and the weight parameter is used to indicate the importance of the information item when it is used to determine the first safe passage probability.

In a possible implementation manner, the first model is a model trained based on at least one sample data, and the sample data includes information extracted from the layer information of the second road section and the vehicle attribute information of the target vehicle. At least one information item, the second road section is a road section adjacent to the first road section, and the exit of the second road section is the entrance of the first road section. In the implementation of the embodiments of this application, since adjacent road sections have the same or similar characteristics, the first model trained based on the layer information of the second road section and the vehicle attribute information of the target vehicle can be improved, which can improve the calculation of the first model by the first model. 1. The accuracy of the probability of safe passage.

In a possible implementation manner, the layer information of the first road section includes at least one of static layer information of the first road section and dynamic layer information of the first road section; wherein, the first road section The static layer information of a road section is used to indicate the infrastructure information of the first road section; the dynamic layer information of the first road section is used to indicate the dynamic traffic information of the first road section. To implement the embodiment of this application, the cloud server can obtain the layer information of the first road segment in the automatic driving strategy layer based on the itinerary information reported by the target vehicle, and then, according to the layer information of the first road segment and the attribute information of the vehicle Obtain a driving strategy that meets the safety requirements, so that the target vehicle can drive automatically according to the driving strategy determined by the cloud server. Since the automatic driving strategy layer contains more information, the static layer information of the road ensures that the automatic driving can efficiently determine the driving path and avoid obstacles, and the dynamic layer information of the road ensures that the automatic driving can respond in time In emergencies, this implementation method can overcome the sensor’s perception defects (for example, the sensor’s perception defects are reflected in the limited data acquired by the sensor, the limited detection range of the sensor, the detection of the sensor is easily affected by the environment, etc.), which can improve Determining the accuracy of driving strategies that meet safety requirements reduces the risk of autonomous driving.

In a possible implementation, the static layer information of the first road section includes at least one of lane attributes, digital device information, and green belt information; the dynamic layer information of the first road section includes weather information, Road surface information, the congestion situation of the first road section in the first time period, the probability of pedestrians and non-motorized vehicles passing through the first road section in the first time period, the first road section in the first time period At least one of the accident probabilities of a driving accident occurring within the time period. In the implementation of the embodiment of this application, the static layer information of the road ensures that the automatic driving can efficiently determine the driving path and avoid obstacles, and the dynamic layer information of the road ensures that the automatic driving can respond to sudden information in a timely manner. This implementation method It can overcome the sensor's perception defects (for example, the sensor's perception defects are reflected in the limited data acquired by the sensor, the limited detection range of the sensor, the detection of the sensor is easily affected by the environment, etc.), which can improve the determination of the driving strategy that meets the safety requirements. Accuracy reduces the risk of autonomous driving.

In a possible implementation manner, the first road section is the road section where the target vehicle is driving; the method further includes: obtaining a second driving strategy for the target vehicle driving on the first road section; wherein , The second driving strategy is determined by the target vehicle according to sensor data obtained in real time; in the case where the similarity between the first driving strategy and the second driving strategy is less than a first threshold, the target vehicle The vehicle sends prompt information for switching the second driving strategy to the first driving strategy. In the implementation of the embodiments of this application, since the automatic driving strategy layer contains rich information and can overcome the sensor’s perception defects, this also means that the cloud server determines the safety requirements of the first driving strategy based on the automatic driving strategy layer. A second driving strategy that is higher than the target vehicle determined based on real-time sensor data. For the purpose of safe driving, when the cloud server determines that the similarity between the first driving strategy and the second driving strategy is less than the set threshold, it sends to the target vehicle an instruction to switch the second driving strategy to the first driving strategy. Prompt information, so that the target vehicle adopts a safer first driving strategy for automatic driving, which can reduce the risk of automatic driving.

In a possible implementation manner, the vehicle attribute information of the target vehicle includes at least one of the automatic driving capability of the target vehicle, sensor distribution information of the target vehicle, and the driving state of the driver in the target vehicle. Kind.

In a second aspect, an embodiment of the present application provides an automatic driving method, which is applied to a vehicle-mounted terminal on a target vehicle, and the method includes: receiving a first driving strategy sent by a cloud server; wherein, the first driving strategy is The first driving strategy obtained by the method described in the above first aspect; the target vehicle is automatically driven according to the first driving strategy.

To implement the embodiment of this application, after the cloud server determines the first driving strategy according to the automatic driving strategy layer, it sends the first driving strategy to the target vehicle so that the target vehicle can perform automatic driving according to the first driving strategy determined by the cloud server . Since the information contained in the autonomous driving strategy layer is richer and can overcome the sensor’s perception defects (for example, the sensor’s perception defects are reflected in the limited data acquired by the sensor, the limited detection range of the sensor, and the detection of the sensor is easily affected by the environment. Etc.), which can improve the accuracy of determining driving strategies that meet safety requirements and reduce the risk of autonomous driving.

In a possible implementation manner, the method further includes: acquiring a second driving strategy for the target vehicle driving on the first road section through sensor data; and performing the operation on the target vehicle according to the first driving strategy Automatic driving includes: performing automatic driving on the target vehicle according to the first driving strategy and the second driving strategy. By implementing the embodiments of this application, when the target vehicle is driving on the first road segment, the target vehicle can obtain the second driving strategy according to the sensor data obtained in real time, and automatically drive the target vehicle based on the first driving strategy and the second driving strategy. Preventing the target vehicle from adopting a low-safety driving strategy to automatically drive the target vehicle can reduce the risk of automatic driving.

In a possible implementation manner, the automatic driving of the target vehicle according to the first driving strategy and the second driving strategy includes: after determining the first driving strategy and the second driving strategy When the similarity of the driving strategy is greater than the first threshold, the target vehicle is automatically driven according to the first driving strategy, or the target vehicle is automatically driven according to the second driving strategy; In a case where the similarity between the first driving strategy and the second driving strategy is less than the first threshold, the target vehicle is automatically driven according to the first driving strategy. In the implementation of the embodiments of this application, since the automatic driving strategy layer contains rich information and can overcome the sensor’s perception defects, this also means that the cloud server determines the safety requirements of the first driving strategy based on the automatic driving strategy layer. A second driving strategy that is higher than the target vehicle determined based on real-time sensor data. For the purpose of safe driving, when the target vehicle determines that the similarity between the first driving strategy and the second driving strategy is greater than the set threshold, the target vehicle is automatically driven according to the first driving strategy, or according to the first driving strategy. The second driving strategy automatically drives the target vehicle; when it is determined that the similarity between the first driving strategy and the second driving strategy is less than the set threshold, the target vehicle is automatically driven according to the first driving strategy. Through this implementation method, the target vehicle can be prevented from adopting a low-safety driving strategy to automatically drive the target vehicle, and the risk of automatic driving can be reduced.

In the third aspect, an embodiment of the present application provides a cloud server, and the cloud server may include:

The receiving unit is configured to receive the vehicle attribute information reported by the target vehicle and the travel information of the target vehicle; wherein the vehicle attribute information of the target vehicle is used to generate an automatic driving strategy; the first acquiring unit is used to generate an automatic driving strategy according to the travel information Acquire the layer information of the first road section on which the target vehicle is traveling in the automatic driving strategy layer; the second acquisition unit is configured to acquire the first road section according to the layer information of the first road section and the vehicle attribute information of the target vehicle A driving strategy; a first sending unit, configured to send the first driving strategy to the target vehicle.

In a possible implementation manner, the cloud server stores a corresponding relationship between a driving strategy and a safe passage probability; the second acquisition unit includes a safe passage probability acquisition unit and a driving strategy acquisition unit, wherein the safe passage probability The obtaining unit is configured to obtain the first safe passage probability according to the layer information of the first road section and the vehicle attribute information of the target vehicle; the driving strategy obtaining unit is used to obtain the first safe passage probability according to the driving strategy and the safe passage probability The corresponding relationship obtains the first driving strategy corresponding to the first safe passage probability.

In a possible implementation manner, the safe passage probability acquisition unit is specifically configured to: calculate the first safe passage probability through a first model, where the first model includes at least one information item and the at least one The weight parameter corresponding to the information item, the at least one information item is an information item extracted according to the layer information of the first road section and the vehicle attribute information of the target vehicle, and the weight parameter is used to indicate that the information item is used The degree of importance in determining the first safe passage probability.

In a possible implementation manner, the first model is a model trained based on at least one sample data, and the sample data includes information extracted from the layer information of the second road section and the vehicle attribute information of the target vehicle. At least one information item, the second road section is a road section adjacent to the first road section, and the exit of the second road section is the entrance of the first road section.

In a possible implementation manner, the layer information of the first road section includes at least one of static layer information of the first road section and dynamic layer information of the first road section; wherein, the first road section The static layer information of a road section is used to indicate the infrastructure information of the first road section; the dynamic layer information of the first road section is used to indicate the dynamic traffic information of the first road section.

In a possible implementation, the static layer information of the first road section includes at least one of lane attributes, digital device information, and green belt information; the dynamic layer information of the first road section includes weather information, Road surface information, the congestion situation of the first road section in the first time period, the probability of pedestrians and non-motorized vehicles passing through the first road section in the first time period, the first road section in the first time period At least one of the accident probabilities of a driving accident occurring within the time period.

In a possible implementation, the first road section is the road section where the target vehicle is driving; the cloud server further includes: a third acquiring unit configured to acquire that the target vehicle is driving on the first road section The second driving strategy on the above; wherein, the second driving strategy is determined by the target vehicle based on sensor data obtained in real time; the second sending unit is used to determine the difference between the first driving strategy and the second driving strategy In a case where the similarity of is less than the first threshold, sending prompt information for switching the second driving strategy to the first driving strategy to the target vehicle.

In a fourth aspect, an embodiment of the present application provides an automatic driving device, which is applied to a vehicle-mounted terminal on a target vehicle. The device may include: a receiving unit for receiving a first driving strategy sent by a cloud server; The first driving strategy is the first driving strategy obtained by the method described in the first aspect; the control unit is configured to automatically drive the target vehicle according to the first driving strategy.

In a possible implementation, the device further includes: a second driving strategy acquisition unit, configured to acquire the second driving strategy of the target vehicle driving on the first road section through sensor data; the control unit specifically uses Yu: Automatically drive the target vehicle according to the first driving strategy and the second driving strategy.

In a possible implementation manner, the control unit is specifically configured to: in a case where it is determined that the similarity between the first driving strategy and the second driving strategy is greater than a first threshold, according to the first driving strategy The target vehicle is automatically driven according to the strategy, or the target vehicle is automatically driven according to the second driving strategy; after judging that the similarity between the first driving strategy and the second driving strategy is less than that of the In the case of the first threshold, the target vehicle is automatically driven according to the first driving strategy.

In a fifth aspect, an embodiment of the present application provides a cloud server, which may include a memory and a processor, and the memory is used to store a computer program that supports a coordinated device to execute the above method, and the computer program includes program instructions. The processor is configured to call the program instructions to execute the method of the first aspect described above.

In a sixth aspect, the embodiments of the present application provide a vehicle-mounted terminal, which may include a memory and a processor, the memory is used to store a computer program that supports a coordinated device to execute the foregoing method, and the computer program includes program instructions, so The processor is configured to call the program instructions to execute the method of the second aspect described above.

In a seventh aspect, the present application provides a chip system that can execute any method involved in the above-mentioned first aspect, so that related functions can be realized. In a possible design, the chip system further includes a memory, and the memory is used to store necessary program instructions and data. The chip system can be composed of chips, or include chips and other discrete devices.

In an eighth aspect, the present application provides a chip system, which can execute any method involved in the above second aspect, so that related functions can be realized. In a possible design, the chip system further includes a memory, and the memory is used to store necessary program instructions and data. The chip system can be composed of chips, or include chips and other discrete devices.

In a ninth aspect, an embodiment of the present application also provides a computer-readable storage medium, the computer storage medium stores a computer program, and the computer program includes program instructions that, when executed by a processor, cause the processing The device executes the method of the first aspect described above.

In a tenth aspect, an embodiment of the present application also provides a computer-readable storage medium that stores a computer program, and the computer program includes program instructions that, when executed by a processor, cause the processing The device executes the method of the second aspect described above.

In an eleventh aspect, an embodiment of the present application also provides a computer program, the computer program includes computer software instructions, and when the computer software instructions are executed by a computer, the computer executes any one described in the first aspect. An autonomous driving method.

In the twelfth aspect, the embodiments of the present application also provide a computer program, the computer program includes computer software instructions, and when the computer software instructions are executed by a computer, the computer executes any one described in the second aspect. An autonomous driving method.

Description of the drawings

FIG. 1a is a schematic diagram of an automatic driving strategy layer provided by an embodiment of this application;

FIG. 1b is a schematic diagram of another automatic driving strategy layer provided by an embodiment of this application;

FIG. 1c is a schematic diagram of another automatic driving strategy layer provided by an embodiment of this application;

FIG. 1d is a schematic diagram of another automatic driving strategy layer provided by an embodiment of this application;

FIG. 1e is a schematic diagram of a network architecture of an automatic driving system provided by an embodiment of this application;

FIG. 1f is a functional block diagram of an automatic driving device 110 provided by an embodiment of this application;

FIG. 1g is a schematic structural diagram of an automatic driving system provided by an embodiment of this application;

2 is a schematic flowchart of an automatic driving method provided by an embodiment of this application;

FIG. 3a is a schematic flowchart of another automatic driving method provided by an embodiment of this application;

FIG. 3b is a first schematic flowchart of a method for detecting and identifying a target provided by an embodiment of this application;

FIG. 3c is a second schematic flowchart of a method for detecting and identifying a target provided by an embodiment of the application;

3d is a schematic structural diagram of a convolutional neural network model provided by an embodiment of this application;

4 is a schematic flowchart of another automatic driving method provided by an embodiment of this application;

FIG. 5 is a schematic structural diagram of a cloud server provided by an embodiment of this application;

FIG. 6 is a schematic structural diagram of an automatic driving device provided by an embodiment of the application;

FIG. 7 is a schematic structural diagram of another cloud server provided by an embodiment of the application.

Detailed ways

The embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

The terms “first” and “second” in the description of the application and the drawings are used to distinguish different objects or to distinguish different treatments of the same object, rather than describing a specific order of the objects. In addition, the terms "including" and "having" and any variations thereof mentioned in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes other steps or units that are not listed, or optionally also Including other steps or units inherent to these processes, methods, products or equipment. It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used as examples, illustrations, or illustrations. Any embodiment or design method described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being superior or advantageous over other embodiments or design solutions. To be precise, words such as "exemplary" or "for example" are used to present related concepts in a specific manner. In the embodiments of the present application, "A and/or B" means A and B, A or B. "A, and/or B, and/or C" means any one of A, B, and C, or means any two of A, B, and C, or means A, B, and C.

In order to facilitate the understanding of the technical solutions described in this application, some terms in this application are explained below:

(1) Autonomous vehicles (Autonomous vehicles; Self-piloting auto)

In the embodiments of the present application, an autonomous vehicle is also called an unmanned vehicle, a computer-driven vehicle, or a wheeled mobile robot, which is an intelligent vehicle that realizes unmanned driving through a computer system. In practical applications, autonomous vehicles rely on artificial intelligence, visual computing, radar, monitoring devices, and global positioning systems to cooperate to allow computer equipment to automatically and safely operate motor vehicles without any human active operation.

(2) Autonomous driving strategy layer

In the embodiment of the present application, the automatic driving strategy layer is a subset of the automatic driving map and can be used to instruct the vehicle to perform automatic driving. Specifically, the automatic driving strategy layer may include static layer information or dynamic layer information. Each layer can be considered as a specific map. For example, the static layer information can be: the connection relationship between roads, the position of the lane line, the number of lane lines, and other objects around the road, etc.; for another example, the static layer information can be: the information of the traffic sign ( For example, the location and height of traffic lights, the content of the signs, such as speed limit signs, continuous detours, slow driving, etc.), trees around the road, building information, etc. For example, the dynamic layer information may be dynamic traffic information, and the information may be associated with a time point (or time period), or may not be related to a time point (or time period). In some implementation manners, the format of the dynamic layer information may be: timestamp (or time period) + road section + information. For example, weather information of section 1 at a certain time or within a certain period of time, road surface information of section 1 at a certain time or within a certain period of time (for example, road interruption, road maintenance, road spilling, road water accumulation )and many more.

In some implementation manners, the layer information contained in the automatic driving strategy layer may be two-dimensional information or three-dimensional information. In the embodiments of this application, two-dimensional information is also called vector information. The so-called vector is a quantity that has both magnitude and direction. Exemplarily, the two-dimensional information may be coordinate information of the obstacle in the road. In the embodiments of the present application, three-dimensional information refers to some abstract information based on two-dimensional information, and the abstract information is used to reflect the characteristics of an object. Exemplarily, the three-dimensional information may be the coordinate information of the obstacle on the road and the size of the obstacle.

In the embodiment of the present application, the use object of the automatic driving strategy layer is often a vehicle with automatic driving capability.

In the embodiment of the present application, FIGS. 1a to 1d are schematic diagrams of an automatic driving strategy layer provided by an embodiment of the present application. Taking road section 1 as an example, as shown in Figure 1a, the automatic driving strategy layer contains road static layer information such as lane line information, number of lanes, road boundary information, and road driving parameters. Take road section 2 as an example, as shown in Figure 1b, the automatic driving strategy layer contains lane line information, number of lanes, road boundary information, green belt information and other static road layer information, as well as trees lying on the road. Dynamic layer information; Take road section 3 as an example, as shown in Figure 1c, the automatic driving strategy layer contains lane line information, number of lanes, road boundary information, green belt information and other road static layer information, as well as weather information (For example, at time T1, light snow turns to heavy snow) and other dynamic layer information; take road section 4 as an example, as shown in Figure 1d, the automatic driving strategy layer contains lane line information, number of lanes, road boundary information, and green belt The static layer information of roads such as information, digital information equipment, etc., also includes dynamic layer information such as weather information (at T1, sunny to cloudy), historical pedestrian and non-motor vehicle crossing probability 60%, moderate congestion and other dynamic layer information.

In the embodiments of this application, the autonomous driving strategy layer can be regarded as an extension of traditional hardware sensors (for example, radar, laser rangefinder, or camera), which contains more data and is free from environment, obstacles, or The impact of interference. Specifically, the static layer information of the road ensures that the automatic driving can efficiently determine the driving path and avoid obstacles, and the dynamic layer information of the road ensures that the automatic driving can respond to emergencies in a timely manner. This implementation method can effectively overcome the perception defects of traditional hardware sensors. For example, the perception defects of the sensor are reflected in the limited data acquired by the sensor, the limited detection range of the sensor, and the detection of the sensor is easily affected by the environment.

In practical applications, the static layer information of the road can be regarded as a priori information. Specifically, the prior information refers to some information that can be collected in advance and will not change in a short time. In other words, these things exist objectively and will not change with changes in external things, so they can be collected in advance and passed as a priori information to autonomous vehicles to make decisions.

(3) Autonomous driving strategy

At present, there are two levels of auto-driving technology recognized by the global automotive industry, which are respectively proposed by the US Highway Safety Administration (NHTSA) and the International Society of Automata Engineers (SAE). The existing automatic driving level distribution can be shown in Table 1:

Table 1 Autonomous driving classification table

In the embodiment of the present application, the automatic driving strategy may include the automatic driving level, and may also include instructions instructing the vehicle to accelerate, decelerate, advance, stop, and start, and may also include instructing the vehicle's speed, acceleration, movement direction, position, and so on. In actual driving, it is necessary to combine the static layer information of the road and/or the dynamic layer information of the road to determine the specific driving strategy to be used, for example, which automatic driving level to use.

The automatic driving method provided in the embodiments of the present application is applied to other devices (for example, a cloud server) with the function of controlling automatic driving, or applied to a vehicle with the function of automatic driving, which will be specifically introduced below:

In one implementation, the cloud server is used to implement the automatic driving method provided in the embodiments of the present application, and obtain the driving information of the target vehicle through the automatic driving map stored in the cloud server or the automatic driving strategy layer transferred from other devices by the cloud server. The first driving strategy corresponding to the first road section (the first road section is any road section in the itinerary information), and the first driving strategy is sent to the target vehicle. Here, the first driving strategy is used to instruct the target vehicle to follow the first driving strategy Carry out autonomous driving. In addition, when the first road segment is the road segment where the target vehicle is driving, the cloud server can also obtain the second driving strategy of the target vehicle driving on the first road segment, where the second driving strategy is determined by the target vehicle based on real-time sensor data. If the cloud server determines that the similarity between the first driving strategy and the second driving strategy satisfies the preset condition, the cloud server sends to the target vehicle a prompt message for switching the second driving strategy to the first driving strategy. In the embodiment of the present application, the similarity of the first driving strategy and the second driving strategy meeting the preset condition may include that the similarity of the first driving strategy and the second driving strategy meets the set first threshold, or it may include The similarity between the first driving strategy and the second driving strategy satisfies the functional relationship, which is not specifically limited in the embodiment of the present application. In this implementation, the risk of automatic driving of the target vehicle can be reduced.

In one implementation, the target vehicle may receive the autonomous driving map sent by the cloud server, and obtain the first driving strategy corresponding to the first road segment on which the target vehicle is traveling in the autonomous driving map, and then the target vehicle may be subjected to the first driving strategy according to the first driving strategy. Carry out autonomous driving. In addition, for the target vehicle, the target vehicle can also obtain the second driving strategy through real-time sensor data. When it is determined that the similarity between the first driving strategy and the second driving strategy is greater than the set first threshold , The target vehicle can be automatically driven according to the first driving strategy, or the target vehicle can be automatically driven according to the second driving strategy; after judging that the similarity between the first driving strategy and the second driving strategy is less than the set first In the case of a threshold value, the target vehicle is automatically driven according to the first driving strategy. In this implementation, the risk of automatic driving of the target vehicle can be reduced.

Fig. 1e is a schematic diagram of a network architecture of an automatic driving system provided by an embodiment of the present application. As shown in Figure 1e, the autopilot system architecture includes vehicle 10 (ie vehicle 1), vehicle 2 and vehicle M (M is an integer greater than 0. The number of vehicles shown in the figure is an exemplary description and should not constitute a limitation. ) And the cloud server 20. In practical applications, the cloud server 20 may establish a communication connection with multiple vehicles 10 through a wired network or a wireless network.

In the embodiment of the present application, as shown in FIG. 1e, the vehicle 10 includes an automatic driving device 110.

In the embodiment of the present application, the cloud server 20 can control the vehicle 1, the vehicle 2, and the vehicle M through the multi-dimensional data contained in the automatic driving strategy layer by running its stored programs for controlling the automatic driving of automobiles (for example, Instruct the vehicle how to drive through the driving strategy). Programs related to controlling auto-driving cars can be programs that manage the interaction between autonomous vehicles and road obstacles, programs that control the route or speed of autonomous vehicles, and programs that control interaction between autonomous vehicles and other autonomous vehicles on the road.

In some examples, the cloud server obtains the layer information of the road section (for example, the first road section) the vehicle is driving on in the autonomous driving layer, and then sends the information about the driving vehicle according to the layer information of the first road section the vehicle is driving on. The driving strategy suggested by the driving situation on the first road segment, for example, the dynamic layer information of the first road segment (for example, the dynamic layer information contains remnants) to determine the presence of obstacles in front, and tell the autonomous vehicle how to go around Turn it on; for another example, determine the road surface water condition through the dynamic layer information of the first road section (for example, the dynamic layer information contains road surface information), and inform the autonomous vehicle how to drive on the water-filled road. The cloud server sends a response to the autonomous vehicle indicating how the vehicle should travel in a given scene. For example, based on the layer information of the first road section, the cloud server can confirm the existence of a temporary stop sign in front of the road and inform the autonomous vehicle how to bypass the road. Correspondingly, the cloud server sends a suggested operation mode (for example, instructing the vehicle to change lanes on another road) for the automatic driving vehicle to pass through a closed road section (or an obstacle). In practical applications, the cloud server can also send the recommended operation mode to other vehicles in the area that may encounter the same obstacle, so as to assist other vehicles not only to recognize the closed lanes but also to know how to pass.

FIG. 1f is a functional block diagram of the automatic driving device 100 provided by an embodiment of the present application. In some embodiments, the automatic driving device 100 may be configured in a fully automatic driving mode, a partially automatic driving mode, or a manual driving mode. Taking the automatic driving classification proposed by SAE in Table 1 as an example, the fully automatic driving mode can be L5, which means that all driving operations are completed by the vehicle, and the human driver does not need to maintain attention; partially automatic driving modes can be L1, L2, L3, L4, where L1 indicates that the vehicle provides driving for one of the steering wheel and acceleration/deceleration operations, and the human driver is responsible for the rest of the driving operations; L2 indicates that the vehicle provides driving for multiple operations in the steering wheel and acceleration/deceleration, and the human driver is responsible for the rest L3 means that most of the driving operations are completed by the vehicle, and the human driver needs to maintain concentration for emergencies; L4 means that all driving operations are completed by the vehicle, and the human driver does not need to maintain attention, but limits the road and Environmental conditions; the manual driving mode can be L0, which means that a human driver has full authority to drive the car.

In practical applications, the automatic driving device 100 can control itself while in the automatic driving mode, and can determine the current state of the vehicle and the surrounding environment through human operations, determine the possible behavior of at least one other vehicle in the surrounding environment, and determine The confidence level corresponding to the possibility of the other vehicle performing possible behaviors is controlled based on the determined information. When the automatic driving device 110 is in a fully automatic driving mode, the automatic driving device 110 may be set to operate without human interaction.

The automatic driving device 110 may include various subsystems, for example, a traveling system 102, a sensor system 104, a control system 106, one or more peripheral devices 108 and a power supply 110, a computer system 112, and a user interface 116. In some implementations, the autonomous driving device 110 may include more or fewer subsystems, and each subsystem may include multiple elements. In addition, each subsystem and element of the automatic driving device 110 may be interconnected by wire or wirelessly.

In the embodiment of the present application, the traveling system 102 may include components that provide power movement for the automatic driving device 110. In some implementations, the travel system 102 may include an engine 118, an energy source 119, a transmission 120, and wheels/tires 121. The engine 118 may be an internal combustion engine, an electric motor, an air compression engine, or a combination of other types of engines, for example, a hybrid engine composed of a gasoline engine and an electric motor, or a hybrid engine composed of an internal combustion engine and an air compression engine. In practical applications, the engine 118 converts the energy source 119 into mechanical energy.

In the embodiment of the present application, the energy engine 119 may include, but is not limited to: gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, or other power sources. The energy source 119 may also provide energy for other systems of the automatic driving device 110.

In the embodiment of the present application, the transmission device 120 can transmit the mechanical power from the engine 118 to the wheels 121. The transmission device 120 may include a gearbox, a differential, and a drive shaft. In some implementations, the transmission device 120 may also include other devices, such as a clutch. Among them, the drive shaft includes one or more shafts that can be coupled to one or more wheels 121.

In the embodiment of the present application, the sensor 104 may include several sensors that sense information about the environment around the automatic driving device 110. For example, the sensor system 104 may include a positioning system 122 (here, the positioning system may be a GPS system, a Beidou system or other positioning systems), an inertial measurement unit (IMU) 124, a radar 126, and laser ranging仪128 and camera 130. The sensor system 104 may also include sensors of the internal system of the monitored automatic driving device 110, for example, an in-vehicle air quality monitor, a fuel gauge, and an oil temperature gauge. One or more sensor data from these sensors can be used to detect objects and their corresponding characteristics (e.g., position, shape, direction, speed, etc.). These detections and identifications are key functions for the safe operation of the autonomous automatic driving device 110.

In the embodiment of the present application, the positioning system 122 can be used to estimate the geographic location of the automatic driving device 110; the IMU 124 is used to sense the position and orientation changes of the automatic driving device 110 based on inertial acceleration. In some implementations, the IMU 124 may be a combination of an accelerometer and a gyroscope.

In the embodiment of the present application, the radar 126 may use radio signals to sense objects in the surrounding environment of the automatic driving device 110. In some implementations, in addition to sensing the object, the radar 126 may also be used to sense the speed and/or direction of the object.

In the embodiment of the present application, the laser rangefinder 128 can use laser light to sense objects in the environment where the automatic driving device 110 is located. In some implementations, the laser rangefinder 128 may include one or more laser sources, laser scanners, and one or more monitors, as well as other system components.

In the embodiment of the present application, the camera 130 may be used to capture multiple images of the surrounding environment of the automatic driving device 110. In some implementation manners, the camera 130 may be a still camera or a video camera, which is not specifically limited in the embodiment of the present application.

In the embodiment of the present application, the control system 106 can control the operation of the automatic driving device 110 and components. The control system 106 may include various components, including a steering system 132, a throttle 134, a braking unit 136, a computer vision system 140, a route control system 142, and an obstacle avoidance system.

In the embodiment of the present application, the steering system 132 is operable to adjust the forward direction of the automatic driving device 110. For example, it may be a steering wheel system in one embodiment.

In the embodiment of the present application, the throttle 134 is used to control the operating speed of the engine 118 and further control the speed of the automatic driving device 110.

In the embodiment of the present application, the braking unit 136 is used to control the speed of the automatic driving device 110. The braking unit 136 may use friction to slow down the wheels 121. In some implementations, the braking unit 136 may convert the kinetic energy of the wheels 121 into electric current. The braking unit 136 may also take other forms to slow down the rotation speed of the wheels 121 to control the speed of the automatic driving device 110.

In the embodiment of the present application, the computer vision system 140 may be operated to process and analyze the images captured by the camera 130 to identify objects and/or features in the surrounding environment of the automatic driving device 110. In some implementations, the objects and/or features mentioned here may include, but are not limited to: traffic signals, road boundaries, and obstacles. The computer vision system 140 may use object recognition algorithms, structure from motion (SFM) algorithms, visual tracking, and other computer vision technologies. In some implementations, the computer vision system 140 can be used to map the environment, track objects, estimate the speed of objects, and so on.

In the embodiment of the present application, the route control system 142 is used to determine the driving route of the automatic driving device 110. In some implementations, the route control system 142 may combine data from sensors, the positioning system 122, and one or more predetermined maps to determine the driving route for the autonomous driving device 110.

In the embodiment of the present application, the obstacle avoidance system 144 is used to identify, evaluate and avoid or otherwise surpass potential obstacles in the environment of the automatic driving device 110.

It is understood that, in some implementations, the control system 106 may add or alternatively include components other than those shown and described in FIG. 1f. Or you can reduce a part of the components shown above,

In the embodiment of the present application, the automatic driving device 110 interacts with external sensors, other vehicles, other computer systems, or users through the peripheral device 108. The peripheral device 108 may include a wireless communication system 146, an onboard computer 148, a microphone 150, and/or a speaker 152.

In some implementations, the peripheral device 108 provides a means for the user of the autonomous driving device 110 to interact with the user interface 116. For example, the onboard computer 148 may provide information to the user of the automatic driving device 110. The user interface 116 can also operate the onboard computer 148 to receive user input. The on-board computer 148 can be operated through a touch screen. In other cases, the peripheral device 108 may provide a means for the autonomous driving device 110 to communicate with other devices in the vehicle. For example, the microphone 150 may receive audio from the user of the autonomous driving device 110, such as a voice command or other audio input. Similarly, the speaker 150 may output audio to the user of the automatic driving device 110.

In the embodiment of the present application, the wireless communication system 146 may wirelessly communicate with one or more devices directly or via a communication network. For example, the wireless communication system 146 may use 3G cellular communication, such as CDMA, EVDO, GSM/GPRS, or 4G cellular communication, such as LTE. Or 5G cellular communication. In some implementations, the wireless communication system 146 may communicate with a wireless local area network (Wireless local area network, WLAN) by using WIFI. In some implementations, the wireless communication system 146 can directly communicate with the device using an infrared link, Bluetooth, or ZigBee. Other wireless protocols, for example, various vehicle communication systems, such as the wireless communication system 146, may include one or more dedicated short-range communications (DSRC) devices.

In the embodiment of the present application, the power supply 110 may provide power to various components of the automatic driving device 110. In some implementations, the power source 110 may be a rechargeable lithium ion or lead-acid battery. One or more battery packs of such batteries may be configured as a power source to provide power to various components of the automatic driving device 110. In some implementations, the power source 110 and the energy source 119 may be implemented together, for example, the two are configured together as in some all-electric vehicles.

In the embodiment of the present application, part or all of the functions of the automatic driving device 110 are controlled by the computer system 112. The computer system 112 may include at least one processor 113 that executes instructions 115 stored in a non-transitory computer-readable storage medium such as the data storage device 114. The computer system 112 may also be a plurality of computing devices in individual components or subsystems of the automatic driving apparatus 110 that adopt distributed control.

In some implementations, the processor 113 may be any conventional processor, such as a commercially available central processing unit (CPU), or other general-purpose processors or digital signal processors (Digital Signal Processors, DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. Although FIG. 1f functionally shows the processor, memory, and other elements in the same physical housing, those of ordinary skill in the art should understand that the processor, computer system, or memory, or including the processor, the computer system, or the memory, may not be stored in the same physical enclosure. Multiple processors, computer systems, or memories in a housing. For example, the memory may be a hard disk drive, or other storage medium located in a different physical enclosure. Therefore, a reference to a processor or computer system will be understood to include a reference to a collection of processors or computer systems or memories that may operate in parallel, or a reference to a collection of processors or computer systems or memories that may not operate in parallel. Rather than using a single processor to perform the steps described herein, some components such as the steering component and the deceleration component may each have its own processor that only performs calculations related to the function of the specific component.

In the various aspects described herein, the processor 113 may be located away from the vehicle and wirelessly communicate with the vehicle. In other respects, some of the processes described herein are executed on a processor in the vehicle and others are executed by a remote processor, including taking the necessary steps to perform a single operation.

In some implementations, the data storage device 114 may include instructions 115 (for example, program logic), and the instructions 115 may be executed by the processor 113 to perform various functions of the automatic driving device 110, including those described above. The data storage device 114 may also contain additional instructions, including sending data to, receiving data from, interacting with, and/or performing data on one or more of the traveling system 102, the sensor system 104, the control system 106, and the peripheral device 108. Control instructions.

In addition to the instructions 115, the data storage device 114 may also store data, such as road maps, route messages, the location, direction, and speed of the vehicle, and other vehicle data, as well as other information. The above-mentioned information may be used by the automatic driving device 110 and the computer system 112 during the operation of the automatic driving device 110 in the autonomous, semi-autonomous, and/or manual mode.

For example, in the embodiment of the present application, the data storage device 114 obtains the environmental information of the vehicle from the sensor 104 or other components of the automatic driving device 110. The environmental information can be, for example, lane line information, number of lanes, road boundary information, road driving parameters, traffic signals, green belt information, whether there are pedestrians, vehicles, etc. in the environment where the vehicle is currently located. The data storage device 114 may also store state information of the vehicle itself and state information of other vehicles that interact with the vehicle. The status information may include, but is not limited to: the speed, acceleration, and heading angle of the vehicle. For example, based on the speed measurement and distance measurement functions of the radar 126, the vehicle obtains the distance between other vehicles and the speed of other vehicles, and so on. Then, in this case, the processor 113 may obtain the aforementioned vehicle data from the data storage device 114, and determine a driving strategy that meets safety requirements based on the environmental information in which the vehicle is located.

In this embodiment of the present application, the user interface 116 is used to provide information to or receive information from the user of the automatic driving device 110. In some implementations, the user interface 116 may include one or more input/output devices within the set of peripheral devices 108, such as one or more of the wireless communication system 146, the onboard computer 148, the microphone 150, and the speaker 152.

In the embodiment of the present application, the computer system 112 may control the functions of the automatic driving device 110 based on inputs received from various subsystems (for example, the traveling system 102, the sensor system 104, and the control system) and the user interface 116. For example, the computer system 112 may use the input of the incoming air control system 106 to control the steering system 132 so as to avoid obstacles detected by the sensor system 104 and the obstacle avoidance system 144. In some implementations, the computer system 112 is operable to provide control of many aspects of the autonomous driving device 110 and its subsystems.

In some implementations, one or more of the aforementioned components may be installed or associated with the autonomous driving device 110 separately. For example, the data storage device 114 may exist partially or completely separately from the automatic driving device 110. The above-mentioned components may be communicatively coupled together in a wired and/or wireless manner.

In some implementations, the above-mentioned components are just an example. In actual applications, the components in the above-mentioned modules may be added or deleted according to actual needs, and FIG. 1f should not be understood as a limitation to the embodiments of the present application.

An automatic driving vehicle traveling on a road, for example, the automatic driving device 110, may recognize objects in its surrounding environment to determine whether to adjust the current speed of the automatic driving device 110. Here, the object may be other vehicles, traffic control equipment, or other types of objects. In some implementations, each recognized object can be considered independently, and the speed to be adjusted by the autonomous vehicle can be determined based on the respective characteristics of the object, for example, its current driving data, acceleration, and vehicle distance.

In some implementations, the automatic driving device 110 or the computer equipment associated with the automatic driving device 110 (for example, the computer system 112, the computer vision system 140, and the data storage device 114 as shown in FIG. 1f) may be based on the identified object The characteristics of the environment and the state of the surrounding environment (for example, traffic, rain, ice on the road, etc.) to predict the behavior of the recognized object. In some implementations, each recognized object depends on each other's behavior. Therefore, all recognized objects can also be considered together to predict the behavior of a single recognized object. The automatic driving device 110 can adjust its speed based on the predicted behavior of the recognized object. In other words, the automatic driving device 110 can determine what stable state the vehicle will need to adjust to based on the predicted behavior of the object (for example, the adjustment operation may include acceleration, deceleration, or stop). In this process, other factors may also be considered to determine the speed of the automatic driving device 110, such as the lateral position of the automatic driving device 110 on the traveling road, the curvature of the road, and the proximity of static and dynamic objects.

In addition to providing instructions to adjust the speed of the autonomous vehicle, the computer device may also provide instructions to modify the steering angle of the vehicle 100 so that the autonomous vehicle follows a given trajectory and/or maintains an object near the autonomous vehicle (e.g., The safe horizontal and vertical distances of cars in adjacent lanes on the road.

In the embodiment of the present application, the above-mentioned automatic driving device 110 may be a car, a truck, a motorcycle, a bus, a boat, an airplane, a helicopter, a lawn mower, a recreational vehicle, a playground vehicle, a construction equipment, a tram, or a golf ball. Cars, trains, and trolleys, etc., are not particularly limited in the embodiments of the present application.

In some implementation manners, the automatic driving device 110 may further include a hardware structure and/or a software module, and the above functions are realized by driving of a hardware structure, a software module, or a hardware structure and a software module. Whether a certain function of the above-mentioned various functions is executed by a hardware structure, a software module, or a hardware structure plus a software module depends on the specific application of the technical solution and the related constraint conditions.

FIG. 1f illustrates a functional block diagram of the automatic driving device 110, and the automatic driving system 101 in the automatic driving device 110 is introduced below. Fig. 1g is a schematic structural diagram of an automatic driving system provided by an embodiment of the present application. Fig. 1f and Fig. 1g describe the automatic driving device 110 from different perspectives. For example, the computer system 101 in Fig. 1g is the computer system 112 in Fig. 1f. As shown in FIG. 1g, the computer system 101 includes a processor 103, and the processor 103 is coupled to a system bus 105. The processor 103 may be one or more processors, where each processor may include one or more processor cores. A display adapter (video adapter) 107, the display adapter can drive the display 109, and the display 109 is coupled to the system bus 105. The system bus 105 is coupled with an input/output (I/O) bus 113 through a bus bridge 111. The I/O interface 115 is coupled to the I/O bus. The I/O interface 115 communicates with various I/O devices, such as an input device 117 (such as a keyboard, a mouse, a touch screen, etc.), and a media tray 121 (such as a CD-ROM, a multimedia interface, etc.). Transceiver 123 (can send and/or receive radio communication signals), camera 155 (can capture scene and dynamic digital video images) and external USB interface 125. Wherein, optionally, the interface connected to the I/O interface 115 may be a USB interface.

The processor 103 may be any conventional processor, including a reduced instruction set computing ("RISC") processor, a complex instruction set computing ("CISC") processor, or a combination of the foregoing. Alternatively, the processor may be a dedicated device such as an application specific integrated circuit ("ASIC"). Optionally, the processor 103 may be a neural network processor or a combination of a neural network processor and the foregoing traditional processors.

Optionally, in various embodiments described herein, the computer system 101 may be located far away from the autonomous vehicle and may communicate with the autonomous vehicle 100 wirelessly. In other respects, some of the processes described herein are executed on a processor provided in an autonomous vehicle, and others are executed by a remote processor, including taking actions required to perform a single manipulation.

The computer 101 can communicate with the software deployment server 149 through the network interface 129. The network interface 129 is a hardware network interface, such as a network card. The network 127 may be an external network, such as the Internet, or an internal network, such as an Ethernet or a virtual private network (VPN). Optionally, the network 127 may also be a wireless network, such as a WiFi network, a cellular network, and so on.

The hard disk drive interface is coupled to the system bus 105. The hardware drive interface is connected with the hard drive. The system memory 135 is coupled to the system bus 105. The data running in the system memory 135 may include the operating system 137 and application programs 143 of the computer 101.

The operating system includes a shell (Shell) 139 and a kernel (kernel) 141. Shell 139 is an interface between the user and the kernel of the operating system. The shell is the outermost layer of the operating system. The shell manages the interaction between the user and the operating system: waiting for the user's input, interpreting the user's input to the operating system, and processing the output of various operating systems.

The kernel 141 is composed of those parts of the operating system that are used to manage memory, files, peripherals, and system resources. Directly interact with the hardware. The operating system kernel usually runs processes and provides inter-process communication, providing CPU time slice management, interrupts, memory management, IO management, and so on.

Application programs 141 include programs related to controlling auto-driving cars, such as programs that manage the interaction between autonomous vehicles and obstacles on the road, programs that control the route or speed of autonomous vehicles, and programs that control interaction between autonomous vehicles and other autonomous vehicles on the road. . The application 141 also exists on the deploying server 149 system. In one embodiment, when the application program 141 needs to be executed, the computer system 101 may download the application program 141 from the deploying server 14.

For another example, the application program 141 may be an application program that controls the vehicle to calculate a driving strategy based on sensor data obtained in real time. Among them, the sensor data acquired in real time may include environmental information, the state information of the target vehicle itself, and the state information of the target vehicle's potential interaction target objects. Specifically, the environmental information is information about the current environment of the target vehicle (for example, the distribution of green belts, lanes, traffic lights, etc.), and the status information may include, but is not limited to, the speed, acceleration, and heading angle of the vehicle. For example, based on the speed and distance measurement functions of the radar 126, the vehicle obtains the distance between other vehicles and the speed of other vehicles, and so on. The processor 103 of the computer system 101 can call the application 141 to obtain the second driving strategy.

In some implementations, since the information contained in the automatic driving strategy layer is richer, the static layer information of the road ensures that the automatic driving can efficiently determine the driving path and avoid obstacles, and the dynamic layer information of the road ensures Autonomous driving can respond to emergencies in a timely manner. This implementation method can overcome the sensor’s perception defects. It can be known that the accuracy of the first driving strategy is often higher than that of the second driving strategy. Then, in this case The application program 141 may also be an application program that controls the vehicle to determine the final driving strategy according to the first driving strategy and the second driving strategy sent by the cloud server (here, the second driving determination is determined by the vehicle through sensor data obtained in real time). Specifically, when the similarity between the first driving strategy and the second driving strategy is greater than the set first threshold, the application program 141 determines that the first driving strategy or the second driving strategy is the final driving strategy. In the case that the similarity between the first driving strategy and the second driving strategy is less than the set first threshold, the application program 141 determines that the first driving strategy is the final driving strategy.

The sensor 153 is associated with the computer system 101. The sensor 153 is used to detect the environment around the computer 101. For example, the sensor 153 can detect animals, cars, obstacles, and crosswalks. Further, the sensor can also detect the surrounding environment of the above-mentioned animals, cars, obstacles, and crosswalks, such as: the environment around the animals, for example, when the animals appear around them. Other animals, weather conditions, the brightness of the surrounding environment, etc. Optionally, if the computer 101 is located on a self-driving car, the sensor may be a camera, an infrared sensor, a chemical detector, a microphone, etc. When the sensor 153 is activated, it senses information at preset intervals and provides the sensed information to the computer system 101 in real time.

Optionally, in various embodiments described herein, the computer system 101 may be located far away from the automatic driving device 110, and may perform wireless communication with the automatic driving device 110. The transceiver 123 can send automatic driving tasks, sensor data collected by the sensor 153, and other data to the computer system 101; and can also receive control instructions sent by the computer system 101. The automatic driving device can execute the control instructions from the computer system 101 received by the transceiver 123, and perform corresponding driving operations. In other respects, some of the processes described herein are configured to be executed on a processor in an autonomous vehicle, and others are executed by a remote processor, including taking actions required to perform a single operation.

Based on the system architecture shown in FIG. 1e, the following is a flow diagram of an automatic driving method provided by an embodiment of the present application shown in FIG. 2 to specifically explain how the automatic driving of a vehicle is realized in the embodiment of the present application. Including but not limited to the following steps:

Step S200: The target vehicle reports its own vehicle attribute information and itinerary information to the cloud server.

Step S202: The cloud server receives the vehicle attribute information reported by the target vehicle and the travel information of the target vehicle; wherein the vehicle attribute information of the target vehicle is used to generate an automatic driving strategy.

In the embodiment of the present application, the target vehicle is any one of multiple autonomous vehicles in the system architecture shown in FIG. 1e.

In the embodiment of the present application, the itinerary information is used to indicate the itinerary of the target vehicle. In an example, the itinerary information may include the current position of the target vehicle. For example, the current position is the coordinate value (X, Y) represented by a two-dimensional array, where X is the longitude value and Y is the latitude value; in an example , The travel information can include the starting position and the destination position. For example, the starting position is the coordinate value (X1, Y1) represented by the two-dimensional array, and the destination position is the coordinate value (X2, Y2) represented by the two-dimensional array, where , X1 and X2 are longitude values, Y1 and Y2 are latitude values; in one example, the itinerary information can also be the planned driving section from the starting position to the destination position, and the planned driving section is a continuous and directional vector For example, the starting position is A, the destination position is B, and the planned driving section is a continuous and directional vector line of ACDEB. When the itinerary information is the current location of the target vehicle, the vehicle-mounted terminal may send the itinerary information obtained by the location sensor to the server. When the itinerary information is the starting position and the target position of the target vehicle, or the planned road segment from the starting position to the target position, the target vehicle can be car-mounted navigation (for example, GPS system, Beidou system, or other positioning The itinerary information determined by the system) is sent to the server. For example, the user inputs the starting position and the target position in the car navigation of the target vehicle, and the car navigation determines the starting position and the target position input by the user as the itinerary information. For another example, the user selects a planned driving section from the starting position to the destination position in the car navigation of the target vehicle, and the car navigation determines the planned driving section selected by the user as the itinerary information.

In the embodiment of the present application, the vehicle attribute information is used to indicate an automatic driving strategy. Exemplarily, the automatic driving strategy may include an automatic driving strategy that the target vehicle is capable of supporting. Specifically, the vehicle attribute information of the target vehicle may include at least one of the automatic driving capability of the target vehicle, sensor distribution information of the target vehicle, and the driving state of the driver in the target vehicle. Among them, the automatic driving capability of the target vehicle refers to the highest level of automatic driving that the target vehicle can support. The sensor distribution information of the target vehicle may include the type, number, and placement location of the sensors, and so on. The driving state of the driver may include the fatigue degree of the driver in the vehicle, the driving ability of the driver in the vehicle, and so on.

Next, the sensor distribution information of the target vehicle will be elaborated. For example, the target vehicle contains one or more vehicle speed sensors, which can be distributed inside the target vehicle to detect the driving speed of the vehicle; the target vehicle contains There are one or more acceleration sensors, which can be distributed inside the target vehicle to detect the acceleration of the vehicle during driving. For example, the acceleration is the acceleration in a sudden braking state; the target vehicle contains one or more Video sensor, which can be distributed outside the target vehicle to obtain and monitor image data of the surrounding environment of the vehicle; the target vehicle contains one or more radar sensors, which can be distributed outside the entire target vehicle for use To obtain and monitor the electromagnetic wave data of the surrounding environment of the vehicle, it mainly transmits electromagnetic waves, and then detects the distance between the surrounding objects and the vehicle, the shape of the surrounding objects and other data by receiving the electromagnetic waves reflected by the surrounding objects. Taking multiple radar sensors distributed outside the entire target vehicle as an example, a subset of multiple radar sensors is coupled to the front of the vehicle to locate objects in front of the vehicle. One or more other radar sensors may be located at the rear of the vehicle to locate objects behind the vehicle when the vehicle is backing up. Other radar sensors can be located on the side of the vehicle to locate objects, such as other vehicles, that approach the vehicle from the side. For example, a lidar (light detection and ranging, LIDAR) sensor may be installed on a vehicle, for example, the LIDAR sensor may be installed in a rotating structure installed on the top of the vehicle. Then, the rotating LIDAR sensor 120 can transmit light signals around the vehicle in a 360° mode, so as to continuously map all objects around the vehicle as the vehicle moves. For example, the target vehicle contains imaging sensors such as cameras, video cameras, or other similar image acquisition sensors, which can be installed on the vehicle to capture images as the vehicle moves. Multiple imaging sensors can be placed on all sides of the vehicle to capture images around the vehicle in 360° mode. The imaging sensor can not only capture visible spectrum images, but also infrared spectrum images. For example, the target vehicle contains a Global Positioning System (GPS) sensor (or Beidou system sensor), which can be located on the vehicle to provide the controller with geographic coordinates and coordinate generation time related to the location of the vehicle . GPS includes an antenna for receiving GPS satellite signals and a GPS receiver coupled to the antenna. For example, when an object is observed in an image or by another sensor, GPS can provide the geographic coordinates and time of the discovery of the object.

Taking the driving state of the driver as an example, the fatigue degree of the driver in the target vehicle may be one of mild fatigue, moderate fatigue, severe fatigue, and good state. In practical applications, the driver's facial image can be acquired first, and then the acquired facial image of the driver can be recognized to determine the driver's fatigue level. After the classification result of the driver's facial image is recognized, the driving state of different types of drivers can be characterized by means of enumerated values. For example, 0 means good condition, 1 means mild fatigue, 2 means moderate fatigue, and 3 means severe fatigue. The driving ability of the target vehicle driver may include one of high, medium, and low. In practical applications, the driving ability of the driver can be characterized by indication information. For example, 00 indicates that the driving ability of the driver is "high", 01 indicates that the driving ability of the driver is "medium", and 02 indicates the driving ability of the driver. If it is "low", the driver's driving ability can also be characterized by joint instruction information. It should be noted that the above examples are only examples and should not constitute a limitation.

Step S204: The cloud server obtains the layer information of the first road segment on which the target vehicle is traveling in the automatic driving strategy layer according to the itinerary information.

In the embodiment of the present application, a road section refers to a continuous and directional vector line from point A to point B. For example, point A is the starting position in the travel information, and point B is the destination position in the travel information.

In the embodiment of the present application, the cloud server determines at least one road section to travel according to the itinerary information reported by the target vehicle (for example, the road section is the first road section, and the first road section is only an exemplary description and should not constitute a limitation), and then, Obtain the layer information of the first road segment in the automatic driving strategy layer. Here, the road section being driven may include a road section that is being driven, and may also include a road section that is planned to be driven, which is not specifically limited in the embodiment of the present application.

In some implementation manners, the layer information of the first road section may include static layer information of the first road section, where the static layer information of the first road section is used to indicate the infrastructure information of the first road section. In some implementations, the layer information of the first link includes static layer information of the first link and dynamic layer information of the first link, wherein the static layer information of the first link is used to indicate the basis of the first link Facility information, the dynamic layer information of the first road section is used to indicate the dynamic traffic information of the first road section.

Specifically, the infrastructure information of the road section refers to the infrastructure planned and constructed by the relevant department (for example, the transportation department or the government department) to meet the normal travel of vehicles. For example, the infrastructure information of the first section can include roads (including road grades, such as expressways, trunk roads, branch roads), bridges (including culverts), tunnels (including tunnel mechanical and electrical facilities, such as monitoring facilities, communication facilities, and lighting At least one of facilities, etc.) and traffic safety engineering facilities (including signs, markings, guardrails, etc.). In practical applications, the indicator R can be used to characterize “roads”, the indicator L can be used to characterize “bridges”, the indicator U can be used to characterize “tunnels”, and the indicator J can be used to characterize “traffic safety engineering facilities”. Exemplarily, the static layer information of the first road section may include at least one of the following: lane attributes, road digital equipment information, and green belt information. Among them, the lane attribute is used to indicate the standardized information of the basic road; the digital equipment information of the road is used to indicate the hardware facility information of the road; the green belt information is used to indicate the location of the green belt.

For example, lane attributes may include at least one of the following: lane line information, number of lanes, road boundary information, road driving parameters, traffic signals, and obstacles. Wherein, the lane line information is used to indicate the position of the lane line. For example, the lane line information may include lane width, lane line angle and curvature, etc.; the number of lanes is used to indicate the number of lanes, for example, you can pass left, left, right, and right. The information of the four lanes on the right can distinguish three lanes, so that it can be determined that XX road includes three lanes; the road boundary information is used to indicate the position of the road boundary line, and the road driving parameters are used to indicate the maximum speed limit allowed on the current road section and the driving that can be supported Speed, for example, the speed limit of YY road section is 60KM/h, the driving speed supported by XY road section is 100KM/h-120KM/h, and the driving speed level supported by XY road section is driving speed level 3; traffic signals are used to indicate the progress of the vehicle Direction (for example, stop at red light, go at green light, turn left, turn right, U-turn, etc.), obstacles are used to indicate the boundary of the vehicle.

For example, the digital device information of the road may include at least one of the following: a vehicle-to-outside information exchange system, and a drive test unit. Among them, the information exchange between the vehicle and the outside means vehicle to everything, that is, Vehicle to X (V2X), which is the key technology of the intelligent transportation system, which makes the vehicle and the vehicle, the vehicle and the base station, the vehicle and the roadside unit, the vehicle and the cloud server , Cars and pedestrians can communicate, so that a series of traffic information such as real-time road conditions, road information, and pedestrian information can be obtained. Through the V2X system, the automatic driving device 110 can obtain richer real-time data, which can be used for analysis of real-time traffic information, selection of the best driving route, and so on. A Road Side Unit (RSU) is a device that is installed on the roadside and uses short-range communication technology (for example, Cellular-V2X technology) to communicate with an on-board unit (OBU). Specifically, the target vehicle can interact with digital devices on the road (such as roadside units) and through the network with cloud servers to update the environmental information of the driving section in time to prevent the target vehicle from driving outside the permitted operating area In the non-driving area, for example, on a traffic-controlled road while the target vehicle is still driving on the controlled road.

Specifically, the dynamic traffic information of the road section refers to the traffic information with changing attributes when the vehicle is traveling. In the embodiment of the present application, the dynamic layer information of the first road section may be information associated with a time point (or time period), or information that is not associated with a time point (or time period). Exemplarily, the dynamic layer information of the first road section may include at least one of the following: weather information, road surface information, and the first road section in a certain time period (for example, the first time period, specifically, the first time period may be (8:00-9:00 in the morning) the congestion situation, the probability of pedestrians and non-motorized vehicles passing through the first road section in the first time, and the accident probability of driving accidents on the first road section in the first time period. Here, the congestion situation of the first road segment in the first time period may be a historical congestion situation, or may be a congestion situation in a certain time period in the future predicted based on the historical congestion situation. Similarly, the passing probability of pedestrians and non-motorized vehicles on the first road segment in the first time period may be the historical passing probability, or the passing probability in a certain time period in the future predicted based on the historical passing probability. Similarly, the accident probability of a driving accident occurring in the first time period in the first time period may be the historical accident probability, or the accident probability in a certain time period in the future predicted based on the historical accident probability.

Exemplarily, the weather information may include sunny days, cloudy days, heavy rain, light rain, snowing, and so on.

Exemplarily, the road surface information may include road interruption conditions, road maintenance conditions, road spillage conditions, stagnant water conditions, and so on. For example, if there is a road interruption, it means that the road section cannot be driven. For example, if there is road maintenance, it means that the road section needs to be driven carefully or detoured. In this case, for the purpose of safe driving, it is often necessary to switch from a highly automated driving strategy to a less automated driving strategy. For example, switching from a fully automated driving strategy to a partially automated driving strategy. For example, if there is a road leftover situation, it is further obtained whether the size of the leftover object is greater than a certain threshold. If the size of the leftover object is less than a certain threshold, it means that the impact of the leftover object on the automatic driving strategy is minimal or negligible. For example, if the litter is paper scraps, the road litter that occurs at this time has almost no effect on the driving of the automatic driving strategy. If the size of the litter is greater than a certain threshold, for example, if the litter is a stone, the road litter that occurs at this time has a greater impact on the driving of the autonomous vehicle. In this case, for the purpose of safe driving, it is often necessary to switch from an automatic driving strategy with a high degree of automation to an automatic driving strategy with a low degree of automation.

In the embodiment of the present application, the first road section mentioned above is in a certain time period (for example, the first time period, specifically, the first time period may be the time period of 8:00-9:00 in the morning). Congestion, the probability of pedestrians and non-motorized vehicles passing through the first road segment in the first time period, and the accident probability of driving accidents on the first road segment in the first time period all belong to the statistical values of the first road segment. The so-called statistical value (also called sample value) is a comprehensive description of a certain variable in the sample, or the comprehensive quantitative performance of a certain characteristic of all elements in the sample. The statistical value is calculated from the sample, and it is an estimate of the corresponding parameter value. In some implementations, time-sharing statistics can be performed on vehicles on the first road segment, for example, counting the number of vehicles driving on the first road segment in a certain period of time, and counting the number of vehicles traveling on the first road segment in a certain period of time. The distribution of autonomous driving strategies of vehicles; it is also possible to perform time-sharing statistics on the traffic participants on the first road segment except vehicles, for example, to count the number of pedestrians and non-motorized vehicles on the first road segment in a certain period of time And so on, so as to get the statistical value of the first road section.

In the embodiment of this application, the definition of road congestion can be done by judging whether the traffic flow speed of the road section is less than a set threshold. For example, the threshold may be 10km/h, if the traffic flow speed of the road section is less than 10km/h , Indicating that the road is congested; for another example, the threshold is 20 vehicles/lane, indicating that the road is congested.

Step S206: The cloud server obtains a first driving strategy according to the layer information of the first road section and the vehicle attribute information of the target vehicle.

In some implementation manners, obtaining the first driving strategy according to the layer information of the first link and the vehicle attribute information of the target vehicle may include: obtaining the first driving strategy according to the layer information of the first link and the vehicle attribute information of the target vehicle. Afterwards, the first driving strategy corresponding to the first safe passing probability is obtained according to the corresponding relationship between the driving strategy and the safe passing probability. Specifically, the cloud server stores the corresponding relationship between the driving strategy and the safe passage probability. For example, the cloud server sets the driving strategy corresponding to the safe passage probability of 70% as driving strategy 1, and the cloud server sets the driving strategy setting corresponding to the safe passage probability of 75%. For driving strategy 2, the cloud server sets the driving strategy corresponding to a safe passage probability of 80% as driving strategy 3. The cloud server sets the driving strategy corresponding to a safe passage probability of 85% as driving strategy 4, and the cloud server corresponds to a safe passage probability of 90% The driving strategy is set to driving strategy 5, and the cloud server sets the driving strategy corresponding to a safe passage probability of 95% to driving strategy 6, and so on. Therefore, the first safe passage probability determined by the cloud server according to the layer information of the first road section and the vehicle attribute information of the target vehicle is 90%, and the cloud server finds the first safe passage probability according to the correspondence between the preset driving strategy and the safe passage probability. A driving strategy corresponding to a safe passage probability of 90% is driving strategy 5.

In some implementations, the correspondence between the driving strategy and the safe passage probability stored in the cloud server includes the correspondence between the safe passage probability interval and the driving strategy. Then, in this case, according to the correspondence between the driving strategy and the safe passage probability Obtaining the driving strategy corresponding to the first safe passing probability is specifically: searching for the driving strategy corresponding to the first safe passing probability according to the corresponding relationship between the driving strategy and the safe passing probability interval. Specifically, the cloud server divides the safe passage probability into different probability intervals, and each probability interval corresponds to a driving strategy. For example, the cloud server sets the driving strategy corresponding to the probability interval with a safe passage probability greater than or equal to 70% and less than 75% as driving strategy 1, and so on.

In the embodiment of the present application, the realization process of obtaining the first safe passage probability according to the layer information of the first road section and the vehicle attribute information of the target vehicle may include: calculating the first safe passage probability through the first model. Exemplarily, the first model may be a formula, a function model, a trained neural network model, and so on. It should be understood that there may be multiple ways of calculating the probability of safe passage. In one embodiment, the calculation of the probability of the first safe passage may satisfy a formula or a function model.

In a possible implementation manner, the first model includes at least one information item and a weight parameter corresponding to the at least one information item. Here, the at least one information item is extracted based on the layer information of the first road section and the vehicle attribute information of the target vehicle. For the obtained information item, the weight parameter is used to indicate the importance of the information item when it is used to determine the first safe passage probability. Specifically, the first model can be expressed as:

P=A ₁ *ω ₁ +A _n *ω _n +......+A _n *ω _n (1)

Among them, A1 represents the first information item, ω ₁ represents the weight parameter corresponding to the first information item; A2 represents the second information item, ω ₂ represents the weight parameter corresponding to the second information item; An represents the nth information item Item, ω _n represents the weight parameter corresponding to the nth information item. It should be noted that the larger the weight parameter corresponding to the information item, the higher the importance of the information item when it is used to determine the probability of safe communication.

In one possible implementation, the first model is associated with sample data. In one embodiment, the first model is associated with at least one sample data. In practical applications, the first model may be a neural network model. Exemplarily, the neural network model may be a deep learning neural network model (Deep Neural Networks, DNN), or a recurrent neural network model (Recurrent Neural Networks, RNN), and so on. Taking the first model as the cyclic neural network model RNN as an example, the hidden layer of the RNN contains multiple hidden layer nodes, and each hidden layer node corresponds to an information item. Each hidden layer node has a weight parameter. It is worth noting that the key to training the recurrent neural network model RNN is to determine the training samples. Considering that adjacent road sections often have similar traffic characteristics, the sample data of the recurrent neural network model RNN includes the layer information from the second road section and the target vehicle At least one information item obtained by extracting the vehicle attribute information of the vehicle, wherein the second road section is a road section adjacent to the first road section, and the second road section is an entrance of the first road section. In this implementation, because adjacent road sections have the same or similar traffic characteristics, the first model trained based on the layer information of the second road section and the vehicle attribute information of the target vehicle can be improved. The accuracy of calculating the probability of the first safe passage.

In the embodiment of the present application, a trained recurrent neural network model RNN can be obtained through multiple sample data of the second road section, and the trained recurrent neural network model RNN can be used to characterize the layer information and target vehicle of the second road section. The corresponding relationship between the vehicle attribute information and the safe driving probability. When determining the safe passage probability of the target vehicle driving on another road section (for example, the first road section), the layer information of the first road section can be combined with the vehicle of the target vehicle The attribute information is input into the trained recurrent neural network model to obtain the safe passage probability of the first section.

It should be noted that in the process of obtaining the first driving strategy based on the layer information of the first road segment and the vehicle attribute information of the target vehicle as described above, in some implementation manners, the layer information of the first road segment includes the first road segment. In the case of static layer information of a road segment, the steps of obtaining the first driving strategy according to the layer information of the first road segment and the vehicle attribute information of the target vehicle may include: according to the static layer information of the first road segment and the target vehicle’s The vehicle attribute information acquires the first driving strategy. At this time, when the safe passage probability is calculated by the first model (for example, the first model is a formula), the information items are extracted from the static layer information of the first road section and the vehicle attribute information of the target vehicle, and then each information is obtained The weight parameter corresponding to the item can be calculated to obtain the probability of safe passage. When the safe passage probability is calculated by the first model (for example, the first model is a trained neural network model), the static layer information of the first road section and the vehicle attribute information of the target vehicle are input into the first model to obtain the target vehicle Probability of safe passage for the first section of travel. In some implementations, when the layer information of the first link includes the static layer information of the first link and the dynamic layer information of the first link, according to the layer information of the first link and the vehicle attributes of the target vehicle The steps of implementing the first driving strategy for information acquisition may include: acquiring the first driving strategy according to the static layer information of the first road section, the dynamic layer information of the first road section, and the vehicle attribute information of the target vehicle. At this time, when the safe passage probability is calculated by the first model (for example, the first model is a formula), the information items are extracted from the static layer information of the first road section and the dynamic layer information of the first road section, and then each The weight parameters corresponding to each information item can be calculated to obtain the probability of safe passage. When calculating the safe passage probability through the first model (for example, the first model is a trained neural network model), the static layer information of the first road section, the dynamic layer information of the second road section, and the vehicle attribute information of the target vehicle Input the first model to obtain the safe passage probability of the first section of the target vehicle.

Step S208: The cloud server sends the first driving strategy to the target vehicle.

In the embodiment of the present application, after the cloud server obtains the first safe driving strategy according to the layer information of the first road section and the vehicle attribute information of the target vehicle, the cloud server issues the first safe driving strategy to the automatic driving device of the target vehicle , So that the target vehicle can drive automatically according to the first driving strategy.

To implement the automatic driving method described in the embodiments of this application, the cloud server can obtain the layer information of the first road segment in the automatic driving strategy layer through the itinerary information reported by the target vehicle, and then, according to the layer information of the first road segment And the attribute information of the vehicle to obtain the driving strategy that meets the safety requirements, so that the target vehicle can drive automatically according to the driving strategy determined by the cloud server. Since the automatic driving strategy layer contains more information, the static layer information of the road ensures that the automatic driving can efficiently determine the driving path and avoid obstacles, and the dynamic layer information of the road ensures that the automatic driving can respond in time In emergencies, this implementation method can overcome the sensor’s perception defects (for example, the sensor’s perception defects are reflected in the limited data acquired by the sensor, the limited detection range of the sensor, the detection of the sensor is easily affected by the environment, etc.), which can improve Determining the accuracy of driving strategies that meet safety requirements reduces the risk of autonomous driving.

In some implementations, when the target vehicle is driving on the first road segment, the target vehicle may report the second driving strategy for driving on the first road segment to the cloud server, where the second driving strategy is that the target vehicle passes through as shown in Figure 1f. The sensor data obtained by the sensor 104 in real time is determined. Then, in this case, the cloud server can determine whether the similarity between the first driving strategy and the second driving strategy meets the set preset conditions. When the similarity of the second driving strategy satisfies the set preset condition, a prompt message for switching the second driving strategy to the first driving strategy is sent to the target vehicle.

In the embodiment of the present application, the form of the prompt information may be other forms such as voice, light, or graphic display. Illustratively, taking the form of the prompt information as a voice as an example, the prompt information may be: "The safety of the current driving strategy is low. Please switch the second driving strategy to the first driving strategy". The voice can be high and severe. Taking the form of the prompt information as light as an example, the prompt information may be brighter and with a sense of flicker. Taking the form of the prompt information as an image and text as an example, the image and text can be "high-risk driving".

Exemplarily, the aforementioned similarity between the first driving strategy and the second driving strategy satisfies the preset condition, which may be that the similarity between the first driving strategy and the second driving strategy is less than the set first threshold (for example, The first threshold may be 0.8), and the similarity between the first driving strategy and the second driving strategy satisfies a preset condition, or the similarity between the first driving strategy and the second driving strategy satisfies a functional relationship, and so on.

In this implementation, since the automatic driving strategy layer contains rich information and can overcome the sensor’s perception defects, this also means that the cloud server determines the safety of the first driving strategy based on the automatic driving strategy layer. It is higher than the second driving strategy determined by the target vehicle based on real-time sensor data. For the purpose of safe driving, when the cloud server determines that the similarity between the first driving strategy and the second driving strategy is less than the set threshold, it sends to the target vehicle an instruction to switch the second driving strategy to the first driving strategy. Prompt information, so that the target vehicle adopts a safer first driving strategy for automatic driving, which can reduce the risk of automatic driving.

The method shown in Figure 2 illustrates that the cloud server can obtain the layer information of the first road segment in the automatic driving strategy layer through the itinerary information reported by the target vehicle, and then, according to the layer information of the first road segment and the vehicle’s The attribute information is used to obtain the driving strategy that meets the safety requirements. The following describes an automatic driving method applied to the on-board terminal, and specifically explains how the on-board terminal performs automatic driving according to the first driving strategy. FIG. 3a is an automatic driving method provided by an embodiment of the application. Specifically, the method is after the above step S208, as shown in FIG. 3a, the method may include the following steps:

Step S2010: The target vehicle receives the first driving strategy sent by the cloud server.

Step S2012: The target vehicle automatically drives the target vehicle according to the first driving strategy.

In some implementation manners, the target vehicle may determine the second driving strategy of the target vehicle driving on the first road segment through the sensor data obtained by the sensor shown in FIG. 1f. In this case, the target vehicle is determined according to the first driving strategy. The implementation steps of performing automatic driving may include: performing automatic driving on the target vehicle according to the first driving strategy and the second driving strategy.

Next, a method of how the target vehicle determines the second driving strategy based on the acquired sensor data is specifically described. For example, the sensor data is used to indicate the current environment information of the target vehicle.

In the embodiment of the present application, the sensor data is taken as an example of image data in a driving environment obtained by a camera. Specifically, the image data may be image data of a static target (for example, a green belt), or a dynamic target (for example, , The image data of the vehicle in front).

In the process of identifying a static target or a dynamic target, first of all, it is detected and recognized what type of object the target is. Specifically, referring to Fig. 3b, it is the realization process of detecting and identifying the target. The image data is input into the feature extraction model, and the feature extraction model selects candidate regions in the image, and extracts the features of the candidate regions. After that, the feature extraction model outputs the extracted features, and inputs these features into the classifier. The extracted features are classified and recognized by the classifier, and the classifier outputs the probability of being recognized as the i-th object. Further, a frame selection representation can be performed on the recognized object. Fig. 3b also shows the way to obtain the classifier. Specifically, the training samples are selected, and operations such as feature extraction are performed on the training samples, so that the training process of the training samples can be completed to obtain the classifier.

Among them, the training samples include positive samples and negative samples. A positive sample refers to a sample that is related to the detection and identification object, and a negative sample refers to a sample that is not related to or is low in the detection and identification object. For example, the target detection and recognition process shown in Figure 3b needs to detect whether an object is a vehicle, that is, the detection and recognition object is a vehicle. At this time, the positive sample is the image of the vehicle, and the negative sample is the image of other objects except the vehicle. For example, the negative sample is the image of the lane line, or the image of the green belt, etc.

In some implementation manners, the above-mentioned feature acquisition model may be a Convolutional Neural Network (CNN) model, and of course, it may also be other learning models with image data feature functions. The classifier may be a support vector machine (Support Vector Machine, SVM), or other types of classifiers may be used. The embodiments of the present application do not limit the types of classifiers and feature extraction models.

Taking a deep learning algorithm to detect and recognize targets, and the feature extraction model is a neural network model as an example, refer to FIG. 3c, which is a schematic structural diagram of a convolutional neural network model provided by an embodiment of this application, wherein the convolutional neural network model It includes a convolutional layer, a pooling layer and three fully connected layers. As shown in Figure 3c, the target detection and recognition process is specifically as follows: the image is input to the convolutional neural network model, and the feature map of the candidate region of the image data is obtained through the convolutional layer of the model. The feature map is used to represent the candidate region from the The extracted features. After that, the pooling layer performs a pooling operation according to the feature map output by the convolutional layer, retains the main features of the candidate area, reduces the number of required calculation features, and reduces the calculation amount of the convolutional neural network model. After that, the feature vector output by the pooling layer is input to the fully connected layer, and the features of each candidate area are synthesized by the fully connected to obtain the features of the entire image, and the image features are output to the classifier. The classifier can output the classification probability of the object in the image. For example, the probability that the object in the output image of the classifier is a vehicle is 98%. In some implementation manners, after the classification probability of the object is obtained, frame selection is performed on the recognized object, and the position of the frame selection is finely trimmed using a regression.

It should be noted that in the convolutional neural network model, there may be multiple convolutional layers, multiple pooling layers, and multiple fully connected layers. For example, multiple convolutional layers/pooling layers as shown in FIG. 3d are in parallel, and the respectively extracted features are input to the neural network layer 130 for processing.

Then, in this case, the target vehicle may obtain the second driving strategy according to the acquired image data of one or more static targets and one or more dynamic target image data in the current driving environment.

In the embodiment of the present application, the realization process of the automatic driving of the target vehicle according to the first driving strategy and the second driving strategy can refer to the two situations described below:

Case 1: When it is judged that the similarity between the first driving strategy and the second driving strategy is greater than the first threshold, the target vehicle is automatically driven according to the first driving strategy, or the target vehicle is automatically driven according to the second driving strategy drive.

In the embodiments of this application, similarity is also called similarity measure, which is a measure for comprehensively evaluating the similarity between two things. It is understandable that the closer two things are, the greater their similarity. Exemplarily, the target vehicle determines that the autonomous driving strategy of the first road segment driven by the target vehicle is fully autonomous L5 through real-time sensor data, and the cloud server determines that the autonomous driving strategy of the first road segment driven is altitude according to the automatic driving strategy layer Autopilot. It can be known from Table 1 that fully autonomous driving L5 means that all driving operations are completed by the vehicle, and the human driver does not need to maintain attention. Highly automated driving L4 refers to the completion of all driving operations by the vehicle. The human driver does not need to maintain attention, but limits the road and environmental conditions. The difference between these two driving strategies is reflected in the fact that the highly automated driving L4 limits the road and environmental conditions, while the fully automated driving L5 does not limit the road and environmental conditions. It is understandable that the similarity between highly automated driving L4 and fully automated driving L5 is extremely high. Illustratively, the similarity calculation formula (for example, the Euclidean distance formula) is used to determine the difference between these two driving strategies. The similarity is 0.85, which is greater than the set first threshold (for example, the first threshold is 0.8). In this case, the target vehicle can automatically drive the target vehicle according to the first driving strategy, or the target The vehicle automatically drives the target vehicle according to the second driving strategy.

It should be noted that, in some implementation manners, in the case where it is determined that the similarity between the first driving strategy and the second driving strategy is greater than or equal to the first threshold, the target vehicle can automatically drive the target vehicle according to the first driving strategy. Or, the target vehicle can automatically drive the target vehicle according to the second driving strategy, which indicates that the safety of the vehicle can be guaranteed when the target vehicle is automatically driven through the first driving strategy or the second driving strategy.

Case 2: In a case where it is determined that the similarity between the first driving strategy and the second driving strategy is less than the first threshold, the target vehicle is automatically driven according to the first driving strategy.

In the embodiments of the present application, due to the sensor’s perception defect, for example, the perception defect is embodied in: limited data acquired by the sensor, limited detection range of the sensor, detection of the sensor is easily affected by the environment, etc., the perception defect easily leads to the acquisition of The sensor data is not accurate enough, which can easily reduce the accuracy of the driving strategy determined based on the sensor data. The autonomous driving strategy layer can be regarded as an extension of traditional hardware sensors (for example, radar, laser rangefinder or camera), which contains more data and is not affected by the environment, obstacles or interference. Among them, the road The static layer information ensures that the automatic driving can efficiently determine the driving path and avoid obstacles. The dynamic layer information of the road ensures that the automatic driving can respond to emergencies in time, so it can be known that according to the automatic driving strategy map The safety of the first driving strategy determined by the level is higher than that of the second driving strategy determined by the target vehicle through real-time sensor data. Based on this, when the automatic driving device determines that the similarity between the first driving strategy and the second driving strategy is less than the first threshold, for the purpose of safe driving, the target vehicle is automatically driven according to the first driving strategy to avoid Unnecessary driving risks.

It should be noted that, in some implementations, in the case where it is determined that the similarity between the first driving strategy and the second driving strategy is less than or equal to the first threshold, the target vehicle automatically drives the target vehicle according to the first driving strategy, and That is, in order to avoid accidental risks of the vehicle, the target vehicle chooses the safer first driving strategy for automatic driving.

It is understandable that in the embodiment of the present application, in the case of "the similarity between the first driving strategy and the second driving strategy is equal to the first threshold", it can be considered that it satisfies the first situation described above, and it can also be considered that it satisfies the first situation described above. If the second situation described above is satisfied, the embodiment of the present application does not specifically limit it.

The method shown in Figure 2 illustrates that the cloud server can obtain the layer information of the first road segment in the automatic driving strategy layer through the itinerary information reported by the target vehicle, and then, according to the layer information of the first road segment and the vehicle’s Attribute information is used to obtain driving strategies that meet safety requirements. The following describes an automatic driving method applied to a vehicle terminal (also known as an automatic driving device), and specifically explains how the vehicle terminal obtains driving according to the automatic driving strategy layer sent by the cloud server Strategically, FIG. 4 is another automatic driving method provided by an embodiment of this application. As shown in FIG. 4, the method may include the following steps:

Step S400: The target vehicle reports the itinerary information to the cloud server.

In the embodiment of the present application, for the specific description of the itinerary information and the specific implementation of how the target vehicle reports the itinerary information to the cloud server, please refer to the related description of FIG. 2 of the foregoing method embodiment, and the details are not repeated here.

Step S402: The cloud server receives the itinerary information reported by the target vehicle.

Step S404: The cloud server obtains the layer information of the first road section on which the target vehicle is traveling in the automatic driving strategy layer according to the itinerary information.

In some implementation manners, the layer information of the first road section may include the static layer information of the first road section, where the static layer information of the first road section is used to indicate the infrastructure information of the first road section. In some implementations, the layer information of the first link includes the static layer information of the first link and the dynamic layer information of the first link. Specifically, the static layer information of the first link is used to indicate the information of the first link. Infrastructure information, the dynamic layer information of the first road section is used to indicate the dynamic traffic information of the first road section.

Step S406: The cloud server sends the layer information of the first road section to the target vehicle.

Step S408: The target vehicle receives the layer information of the first road section, and obtains the first driving strategy according to the layer information of the first road section and the vehicle attribute information of the target vehicle.

In the embodiment of the present application, for the specific implementation of obtaining the first driving strategy by the target vehicle according to the layer information of the first road segment and the vehicle attribute information of the target vehicle, reference may be made to the relevant description of the foregoing step S206, and no further description is provided here.

Step S4010: The target vehicle automatically drives the target vehicle according to the first driving strategy.

In some implementations, the target vehicle may report the first driving strategy obtained according to the layer information of the first road segment and the vehicle attribute information of the target vehicle to the cloud server, so that the cloud server can save it and update the first road segment. Corresponding layer information in the autonomous driving strategy layer. In practical applications, the cloud server may send the first driving strategy (for example, the recommended operating mode) to other vehicles that are the same or similar to the target vehicle in the area, so as to assist other vehicles in driving.

To implement the embodiments of this application, the cloud server can obtain the layer information of the first road segment in the automatic driving strategy layer based on the itinerary information reported by the target vehicle, and then send the layer information of the first road segment to the target vehicle, and then The target vehicle can obtain a driving strategy that meets safety requirements according to the layer information of the first road section and the attribute information of the vehicle, so that it can perform automatic driving according to the determined driving strategy. Since the automatic driving strategy layer contains more information, the static layer information of the road ensures that the automatic driving can efficiently determine the driving path and avoid obstacles, and the dynamic layer information of the road ensures that the automatic driving can respond in time In emergencies, this implementation method can overcome the sensor’s perception defects (for example, the sensor’s perception defects are reflected in the limited data acquired by the sensor, the limited detection range of the sensor, the detection of the sensor is easily affected by the environment, etc.), which can improve Determining the accuracy of driving strategies that meet safety requirements reduces the risk of autonomous driving.

In order to facilitate better implementation of the above-mentioned solutions of the embodiments of the present application, the embodiments of the present application provide a cloud server, which is used to execute the unit of the method according to any one of the foregoing first aspects to pass the automatic driving strategy The layer determines the driving strategy. Specifically, please refer to Fig. 5, which is a schematic block diagram of a cloud server 500 provided by an embodiment of the present application. The cloud server 50 of the embodiment of the present application may include:

The receiving unit 500 is configured to receive vehicle attribute information reported by a target vehicle and travel information of the target vehicle; wherein the vehicle attribute information of the target vehicle is used to generate an automatic driving strategy;

The first acquiring unit 502 is configured to acquire the layer information of the first road segment on which the target vehicle is traveling in the automatic driving strategy layer according to the itinerary information;

The second obtaining unit 504 is configured to obtain a first driving strategy according to the layer information of the first road section and the vehicle attribute information of the target vehicle;

The first sending unit 506 is configured to send the first driving strategy to the target vehicle.

In a possible implementation manner, the cloud server stores a corresponding relationship between a driving strategy and a safe passage probability; the second obtaining unit 504 may include a safe passage probability obtaining unit and a driving strategy obtaining unit, where:

The safe passage probability obtaining unit is configured to obtain the first safe passage probability according to the layer information of the first road section and the vehicle attribute information of the target vehicle;

The driving strategy obtaining unit is configured to obtain a first driving strategy corresponding to the first safe passing probability according to the corresponding relationship between the driving strategy and the safe passing probability.

In a possible implementation manner, the safe passage probability obtaining unit is specifically configured to:

The first safe passage probability is calculated using a first model, wherein the first model includes at least one information item and a weight parameter corresponding to the at least one information item, and the at least one information item is based on the first road section The layer information of and the information item obtained by extracting the vehicle attribute information of the target vehicle, and the weight parameter is used to indicate the importance of the information item when it is used to determine the first safe passage probability.

In a possible implementation manner, the first road section is the road section where the target vehicle is driving; the cloud server 50 further includes:

The third acquiring unit 508 is configured to acquire a second driving strategy of the target vehicle driving on the first road section; wherein, the second driving strategy is determined by the target vehicle according to sensor data acquired in real time;

The second sending unit 5010 is configured to send to the target vehicle to switch the second driving strategy to the second driving strategy when the similarity between the first driving strategy and the second driving strategy is less than a first threshold. Tips for the first driving strategy.

It should be noted that the cloud server described in the embodiment of the present application can refer to the related description of the automatic driving method in the method embodiment described in FIG. 3a and FIG. 4, which will not be repeated here.

In order to facilitate better implementation of the above-mentioned solutions of the embodiments of the present application, the embodiments of the present application provide an automatic driving device, which is used to execute the unit of the method according to any one of the foregoing second aspects to perform the The first driving strategy determined by the server is for automatic driving. Specifically, please refer to FIG. 6, which is a schematic block diagram of an automatic driving device 60 according to an embodiment of the present application. The automatic driving device 60 of the embodiment of the present application may include:

The receiving unit 600 is configured to receive a first driving strategy sent by a cloud server; wherein the first driving strategy is the first driving strategy obtained by the method according to any one of the first aspects;

The control unit 602 is configured to automatically drive the target vehicle according to the first driving strategy.

In a possible implementation manner, the device 60 may further include:

The second driving strategy acquisition unit 604 is configured to acquire the second driving strategy of the target vehicle driving on the first road section through sensor data;

The control unit 602 is specifically configured to:

The target vehicle is automatically driven according to the first driving strategy and the second driving strategy.

In a possible implementation manner, the control unit 602 is specifically configured to:

In the case where it is determined that the similarity between the first driving strategy and the second driving strategy is greater than a first threshold, the target vehicle is automatically driven according to the first driving strategy, or according to the second driving strategy. The driving strategy performs automatic driving on the target vehicle;

In a case where it is determined that the similarity between the first driving strategy and the second driving strategy is less than the first threshold, the target vehicle is automatically driven according to the first driving strategy.

It should be noted that, for the automatic driving device described in the embodiment of the present application, reference may be made to the related description of the automatic driving method in the method embodiment described in FIG. 4, which will not be repeated here.

Please refer to FIG. 7, which is a schematic structural diagram of a cloud server provided by an embodiment of the present application. The cloud server 70 includes at least one processor 701, at least one memory 702, and at least one communication interface 703. In addition, the cloud server may also include general components such as antennas, which will not be described in detail here.

The processor 701 may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the programs in the above scheme.

The communication interface 703 is used to communicate with other devices or communication networks.

The memory 702 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM), or other types that can store information and instructions. The type of dynamic storage device can also be Electrically Erasable Programmable Read-Only Memory (EEPROM), CD-ROM (Compact Disc Read-Only Memory, CD-ROM), or other optical disk storage, CD-ROM Storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or can be used to carry or store desired program codes in the form of instructions or data structures and can be used by Any other medium accessed by the computer, but not limited to this. The memory can exist independently and is connected to the processor through a bus. The memory can also be integrated with the processor.

Wherein, the memory 702 is used to store application program codes for executing the above solutions, and the processor 701 controls the execution. The processor 701 is configured to execute application program codes stored in the memory 702. For example, the code stored in the memory 702 can execute the automatic driving method provided in FIG. 2 or FIG. 3a above.

It should be noted that, for the functions of the device 70 described in the embodiments of the present application, reference may be made to the related descriptions in the method embodiments described in FIG. 2 and FIG. 3a, which will not be repeated here.

The embodiments of the present application also provide a computer storage medium, which stores instructions in the computer-readable storage medium, and when it runs on a computer or a processor, the computer or the processor executes the method described in any of the above embodiments. One or more steps. If each component module of the above device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in the computer readable storage medium. Based on this understanding, the technical solution of the present application is essentially or The part that contributes to the prior art or all or part of the technical solution may be embodied in the form of a software product, and the computer product is stored in a computer-readable storage medium.

The foregoing computer-readable storage medium may be an internal storage unit of the device described in the foregoing embodiment, such as a hard disk or a memory. The above-mentioned computer-readable storage medium may also be an external storage device of the above-mentioned device, such as an equipped plug-in hard disk, a smart memory card (Smart Media Card, SMC), a Secure Digital (SD) card, and a flash card (Flash Card). )Wait. Further, the aforementioned computer-readable storage medium may also include both an internal storage unit of the aforementioned device and an external storage device. The aforementioned computer-readable storage medium is used to store the aforementioned computer program and other programs and data required by the aforementioned device. The aforementioned computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing relevant hardware through a computer program. The computer program can be stored in a computer readable storage medium. At this time, it may include the procedures of the embodiments of the above-mentioned methods. The aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

The steps in the method in the embodiment of the present application can be adjusted, merged, and deleted in order according to actual needs.

The modules in the device of the embodiment of the present application may be combined, divided, and deleted according to actual needs.

It can be understood that those of ordinary skill in the art may realize that the units and algorithm steps of the examples described in the embodiments disclosed in the embodiments of the present application can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.

Those skilled in the art can understand that the functions described in combination with the various illustrative logical blocks, modules, and algorithm steps disclosed in the various embodiments of the present application can be implemented by hardware, software, firmware, or any combination thereof. If implemented in software, the functions described by various illustrative logical blocks, modules, and steps can be stored or transmitted as one or more instructions or codes on a computer-readable medium and executed by a hardware-based processing unit. The computer-readable medium may include a computer-readable storage medium, which corresponds to a tangible medium, such as a data storage medium, or a communication medium that includes any medium that facilitates the transfer of a computer program from one place to another (for example, according to a communication protocol) . In this manner, a computer-readable medium may generally correspond to (1) a non-transitory tangible computer-readable storage medium, or (2) a communication medium, such as a signal or carrier wave. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, codes, and/or data structures for implementing the techniques described in this application. The computer program product may include a computer-readable medium.

Those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed system, device, and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined It can be integrated into another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

In addition, the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.

If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solution of the present application essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disks or optical disks and other media that can store program codes. .

The above are only specific implementations of this application, but the protection scope of this application is not limited to this. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application. Should be covered within the scope of protection of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

An automatic driving method, characterized in that the method is applied to a cloud server, and the method includes:

Receiving vehicle attribute information reported by a target vehicle and travel information of the target vehicle; wherein the vehicle attribute information of the target vehicle is used to generate an automatic driving strategy;

Acquiring the layer information of the first road section on which the target vehicle is traveling in the automatic driving strategy layer according to the itinerary information;

Acquiring a first driving strategy according to the layer information of the first road section and the vehicle attribute information of the target vehicle;

The first driving strategy is sent to the target vehicle.
The method according to claim 1, wherein the cloud server stores the corresponding relationship between the driving strategy and the safe passage probability; the layer information according to the first road section and the vehicle attribute information of the target vehicle Get the first driving strategy, including:

Obtaining a first safe passage probability according to the layer information of the first road section and the vehicle attribute information of the target vehicle;

Acquire the first driving strategy corresponding to the first safe passing probability according to the corresponding relationship between the driving strategy and the safe passing probability.
The method according to claim 2, wherein the obtaining the first safe passage probability according to the layer information of the first road section and the vehicle attribute information of the target vehicle comprises:

The first safe passage probability is calculated using a first model, wherein the first model includes at least one information item and a weight parameter corresponding to the at least one information item, and the at least one information item is based on the first road section The layer information of and the information item obtained by extracting the vehicle attribute information of the target vehicle, and the weight parameter is used to indicate the importance of the information item when it is used to determine the first safe passage probability.
The method of claim 3, wherein the first model is a model trained based on at least one sample data, and the sample data includes layer information from the second road section and vehicle attributes of the target vehicle At least one information item extracted from the information, the second road section is a road section adjacent to the first road section, and an exit of the second road section is an entrance of the first road section.
The method according to any one of claims 1 to 4, wherein the layer information of the first road section includes static layer information of the first road section, and dynamic layer information of the first road section. At least one of; wherein the static layer information of the first road section is used to indicate the infrastructure information of the first road section; the dynamic layer information of the first road section is used to indicate the dynamic traffic of the first road section information.
The method of claim 5, wherein the static layer information of the first road section includes at least one of lane attributes, digital device information, and green belt information; and the dynamic layer information of the first road section Including weather information, road surface information, the congestion situation of the first road section in the first time period, the probability of pedestrians and non-motorized vehicles passing through the first road section in the first time period, and the traffic conditions of the first road section in the first time period. At least one of the accident probabilities of a driving accident occurring in the first time period.
The method according to claim 1, wherein the first road section is a road section where the target vehicle is driving; and the method further comprises:

Acquiring a second driving strategy for the target vehicle driving on the first road segment; wherein the second driving strategy is determined by the target vehicle according to sensor data acquired in real time;

In a case where the similarity between the first driving strategy and the second driving strategy is less than a first threshold, sending prompt information for switching the second driving strategy to the first driving strategy to the target vehicle.
The method according to any one of claims 1-7, wherein the vehicle attribute information of the target vehicle includes the automatic driving capability of the target vehicle, sensor distribution information of the target vehicle, and At least one of the driving conditions of the driver.
An automatic driving method, characterized in that the method is applied to a vehicle-mounted terminal on a target vehicle; the method includes:

Receiving the first driving strategy sent by the cloud server; wherein the first driving strategy is the first driving strategy obtained by the method according to any one of claims 1-8;

The target vehicle is automatically driven according to the first driving strategy.
9. The method according to claim 9, wherein the method further comprises: acquiring a second driving strategy of the target vehicle driving on the first road section through sensor data;

The automatic driving of the target vehicle according to the first driving strategy includes:

The target vehicle is automatically driven according to the first driving strategy and the second driving strategy.
The method of claim 10, wherein the automatic driving of the target vehicle according to the first driving strategy and the second driving strategy comprises:

In the case where it is determined that the similarity between the first driving strategy and the second driving strategy is greater than a first threshold, the target vehicle is automatically driven according to the first driving strategy, or according to the second driving strategy. The driving strategy performs automatic driving on the target vehicle;

In a case where it is determined that the similarity between the first driving strategy and the second driving strategy is less than the first threshold, the target vehicle is automatically driven according to the first driving strategy.
A cloud server, characterized in that the cloud server includes:

A receiving unit, configured to receive vehicle attribute information reported by a target vehicle and travel information of the target vehicle; wherein the vehicle attribute information of the target vehicle is used to generate an automatic driving strategy;

The first acquiring unit is configured to acquire the layer information of the first road segment on which the target vehicle is traveling in the automatic driving strategy layer according to the itinerary information;

The second acquiring unit is configured to acquire the first driving strategy according to the layer information of the first road section and the vehicle attribute information of the target vehicle;

The first sending unit is configured to send the first driving strategy to the target vehicle.
The cloud server according to claim 12, wherein the cloud server stores a corresponding relationship between a driving strategy and a safe passage probability; the second acquisition unit includes a safe passage probability acquisition unit and a driving strategy acquisition unit, wherein,

The safe passage probability obtaining unit is configured to obtain the first safe passage probability according to the layer information of the first road section and the vehicle attribute information of the target vehicle;

The driving strategy obtaining unit is configured to obtain a first driving strategy corresponding to the first safe passing probability according to the corresponding relationship between the driving strategy and the safe passing probability.
The cloud server according to claim 13, wherein the safe passage probability obtaining unit is specifically configured to:

The first safe passage probability is calculated using a first model, wherein the first model includes at least one information item and a weight parameter corresponding to the at least one information item, and the at least one information item is based on the first road section The layer information of and the information item obtained by extracting the vehicle attribute information of the target vehicle, and the weight parameter is used to indicate the importance of the information item when it is used to determine the first safe passage probability.
The cloud server of claim 14, wherein the first model is a model trained based on at least one sample data, and the sample data includes layer information from the second road section and the vehicle of the target vehicle. At least one information item extracted from the attribute information, the second road section is a road section adjacent to the first road section, and an exit of the second road section is an entrance of the first road section.
The cloud server according to any one of claims 12-15, wherein the layer information of the first road section includes static layer information of the first road section and dynamic layer information of the first road section At least one of; wherein the static layer information of the first road section is used to indicate the infrastructure information of the first road section; the dynamic layer information of the first road section is used to indicate the dynamics of the first road section Travel information.
The cloud server of claim 16, wherein the static layer information of the first road section includes at least one of lane attributes, digital device information, and green belt information; and the dynamic layer of the first road section The information includes weather information, road surface information, the congestion situation of the first road section in the first time period, the probability of pedestrians and non-motorized vehicles passing through the first road section in the first time period, and the first road section At least one of the accident probabilities of a driving accident occurring within the first time period.
The cloud server according to claim 12, wherein the first road section is a road section where the target vehicle is driving; and the cloud server further comprises:

The third acquiring unit is configured to acquire a second driving strategy of the target vehicle driving on the first road section; wherein, the second driving strategy is determined by the target vehicle according to sensor data acquired in real time;

The second sending unit is configured to send to the target vehicle to switch the second driving strategy to the second driving strategy when the similarity between the first driving strategy and the second driving strategy is less than a first threshold. 1. Prompt information of driving strategy.
The cloud server according to any one of claims 12-18, wherein the vehicle attribute information of the target vehicle includes the automatic driving capability of the target vehicle, the sensor distribution information of the target vehicle, and the target vehicle At least one of the driving conditions of the driver.
An automatic driving device, characterized in that the device is applied to a vehicle-mounted terminal on a target vehicle; the device includes:

A receiving unit, configured to receive a first driving strategy sent by a cloud server; wherein the first driving strategy is a first driving strategy obtained by the method according to any one of claims 1-8;

The control unit is configured to automatically drive the target vehicle according to the first driving strategy.
The device of claim 20, wherein the device further comprises:

A second driving strategy acquisition unit, configured to acquire the second driving strategy of the target vehicle driving on the first road section through sensor data;

The control unit is specifically used for:

The target vehicle is automatically driven according to the first driving strategy and the second driving strategy.
The device according to claim 21, wherein the control unit is specifically configured to:

In the case where it is determined that the similarity between the first driving strategy and the second driving strategy is greater than a first threshold, the target vehicle is automatically driven according to the first driving strategy, or according to the second driving strategy. The driving strategy performs automatic driving on the target vehicle;

In a case where it is determined that the similarity between the first driving strategy and the second driving strategy is less than the first threshold, the target vehicle is automatically driven according to the first driving strategy.
A cloud server, characterized by comprising a processor and a memory, the processor and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to By calling the program instructions, the method according to any one of claims 1-8 is executed.
A vehicle-mounted terminal, characterized by comprising a processor and a memory, the processor and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to By calling the program instructions, the method according to any one of claims 9-11 is executed.
A chip comprising a processor, a memory, and a communication interface, wherein the chip is configured to execute the method according to any one of claims 1 to 8.
A chip comprising a processor, a memory and a communication interface, characterized in that the chip is configured to execute the method of any one of claims 9-11.
A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program includes program instructions that, when executed by a processor, cause the processor to execute The method described in any one of 1-8 is required.
A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program includes program instructions that, when executed by a processor, cause the processor to execute The method of any one of 9-11 is required.